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농학박사학위논문

Tandem Mass Spectrometry를 활용한 혈청, 소변, 양봉 시료 및 대표작물 중

다종농약다성분 동시분석

Simultaneous Analysis of Pesticide Multiresidues in Human Serum, Urine,

Apiculture Samples, and Representative Crops Using Tandem Mass Spectrometry

2018년 8월

서울대학교 대학원

농생명공학부 응용생명화학전공

신 용 호

A Dissertation for the Degree of Doctor of Philosophy

Simultaneous Analysis of Pesticide Multiresidues in Human Serum, Urine,

Apiculture Samples, and Representative Crops Using Tandem Mass Spectrometry

August 2018

Yongho Shin Applied Life Chemistry Major

Department of Agricultural Biotechnology Seoul National University

i

Abstract

Pesticides are used for the effective control of pests, microorganisms, and

weeds from crops and have contributed to food security. It is necessary to

determine as many pesticides as possible in human, the environment, and

agricultural products due to the intrinsic toxicity and ecotoxicity of pesticides.

In this study, a tandem mass spectrometry coupled to a gas chromatography

(GC-MS/MS) or liquid chromatography (LC-MS/MS) was utilized to

determine approximately four hundreds of pesticides in biological samples

(serum and urine), apiculture samples (bee, pollen, and honey), and

representative crops (pepper, orange, brown rice, and soybean). The scheduled

multiple reaction monitoring (MRM) of the tandem mass spectrometer was

employed in all methodologies to achieve rapid and simultaneous analysis and

to obtain optimal sensitivity and selectivity of target analytes. The preparation

methods for serum and urine were selected by comparing with the three

versions of “Quick, Easy, Cheap, Effective, Rugged, and Safe” (QuEChERS)

procedures. The optimized method was validated for 379 (serum) and 380

(urine) pesticides using LC-MS/MS. As a result, 94.5% (serum) and 95.8%

(urine) of the total pesticides satisfied a limit of quantitation of 10 ng/mL. The

established analytical method was applied to GC-MS/MS amenable pesticides

(54 for serum and 55 for urine) and 53 analytes showed a limit of quantitation

of 10 ng/mL. It was enough low sensitivity to determine pesticides in biological

samples for forensic, clinical, and occupational exposure application.

Apiculture samples, that is, bee (dead, healthy imago, and larva), pollen, and

ii

honey were treated by optimized QuEChERS methods. Among the pesticide

multiresidues, three neonicotinoids (clothianidin, imidacloprid, and

thiamethoxam), which are expected to be totally banned for outdoor use in the

European Union (EU) by the end of 2018, were subjected to method validation.

The limit of quantitation of each analyte was 1 ng/g and it was sufficiently low

to determine pesticide residues below the levels of acute oral toxicity (LD50) of

the bee. The field monitoring was conducted in two area near the apple orchard

and pepper field in 2014. The analysis of the neonicotinoids and 391

multiresidue pesticides in apiculture samples were carried out. Based on residue

levels, comprehensive honey bee exposure near farmland was able to be

understood. Four representative crops were treated using miniaturized

Multiclass Pesticide Multiresidue Method (No. 2) of the Korea Food Code, and

the analytical method was evaluated for 384 pesticides using GC-MS/MS. As

a result, 95.1-99.5% of the total pesticides satisfied the method limit of

quantitation <10 ng/g in the crops, therefore the analytical method obtained the

sufficient detection ability required by positive list system.

Key words: bee product, crop, GC-MS/MS, honey bee, LC-MS/MS,

multiresidue, pesticide, serum, urine

Student Number: 2014-21899

iii

Table of Contents

Abstract.............................................................................................................i

Table of Contents............................................................................................iii

List of Tables.....................................................................................................x

List of Figures................................................................................................xiv

List of Supplementary Information..............................................................xx

Preface..............................................................................................................1

Chapter I. Development and Validation of Pesticide Multiresidue Analysis

in Human Serum and Urine Using LC-MS/MS and GC-MS/MS.................3

Introduction..................................................................................................4

Pesticide intoxication.....................................................................................4

Pesticide analysis in biological samples.........................................................5

Advantage of the tandem mass spectrometry................................................10

Preparation methodology for biological sample...........................................10

Purpose of the present study..........................................................................12

iv

Part 1. Development and Validation of Pesticide Multiresidue Analysis

in Human Serum and Urine Using LC-MS/MS........................................13

Materials and Methods..............................................................................14

Chemicals and reagents................................................................................14

Preparation of standard solutions..................................................................14

LC-MS/MS parameters................................................................................15

Comparison of three versions of QuEChERS...............................................16

Final established sample preparation............................................................17

Validation of analytical methods...................................................................17

Safety information........................................................................................19

Results and Discussion...............................................................................20

Optimization of multiple reaction monitoring (MRM) in LC-MS/MS.........20

Relationship between partition-coefficient and retention time.....................21

Optimization of sample extraction step........................................................26

Method validation........................................................................................38

Limit of quantitation (LOQ)......................................................................38

Linearity of calibration.............................................................................44

Accuracy and precision.............................................................................50

Recovery...................................................................................................64

Matrix effect..............................................................................................79

Conclusions.................................................................................................90

v

Part 2. Development and Validation of Pesticide Multiresidue Analysis

in Human Serum and Urine Using GC-MS/MS.......................................91

Materials and Methods..............................................................................92

Chemicals and reagents................................................................................92

GC-MS/MS instrumental conditions............................................................92

Establishment of scheduled MRM................................................................93

Sample preparation using modified QuEChERS..........................................94

Validation of methodology............................................................................94

Safety information........................................................................................95

Results and Discussion...............................................................................96

Characteristics of pesticide to be studied......................................................96

Optimization of MRM..................................................................................99

Determination of final selected pesticides to be validated..........................102

Validation of analytical method..................................................................103

Limit of quantitation (LOQ) and linearity of calibration.........................103

Accuracy and precision...........................................................................110

Recovery.................................................................................................111

Matrix effect............................................................................................114

Conclusions...............................................................................................115

vi

Chapter II. Analysis of Neonicotinoids (Clothianidin, Imidacloprid, and

Thiamethoxam) and Pesticide Multiresidues in Honey Bee, Pollen, and

Honey Using LC-MS/MS and GC-MS/MS.................................................119

Introduction..............................................................................................120

Benefits from honey bee.............................................................................120

Honey bee Colony Collapse Disorder (CCD) ............................................120

Neonicotinoid, a suspicious chemical leading to CCD...............................121

Analysis of pesticide residues in apiculture samples..................................122

Purpose of the present study........................................................................123

Materials and Methods............................................................................126

Chemicals and reagents..............................................................................126

Preparation of matrix-matched standards...................................................127

Sample collection.......................................................................................127

Instrumental conditions of LC-MS/MS and GC-MS/MS...........................133

LC-MS/MS..............................................................................................133

GC-MS/MS.............................................................................................134

MRM optimization in LC-MS/MS and GC-MS/MS..................................135

Sample preparation.....................................................................................136

Method validation for clothianidin, imidacloprid, and thiamethoxam........137

Pesticide multiresidue screening in bee, pollen, and honey.........................137

Statistical analysis......................................................................................138

Safety information......................................................................................138

vii

Results and Discussion.............................................................................139

Body weights of honey bees.......................................................................139

MRM optimization.....................................................................................141

Method validation for neonicotinoids.........................................................143

Analysis of neonicotinoids (clothianidin, imidaclprid, and thiamethoxam) in

bee, pollen, and honey.................................................................................145

Bee..........................................................................................................145

Pollen......................................................................................................154

Honey......................................................................................................162

Analysis of pesticide multiresidues in bee, pollen, and honey.....................164

Conclusions...............................................................................................180

Chapter III. Multiresidue Analysis for 384 Pesticides in Pepper, Orange,

Brown Rice, and Soybean Using Florisil Solid-phase Extraction and GC-

MS/MS..........................................................................................................183

Introduction..............................................................................................184

Introduction of positive list system.............................................................184

Tandem mass spectrometry for pesticide multiresidue analysis..................186

Solid-phase extraction for pesticide purification........................................192

Multiclass Pesticide Multiresidue Method (No. 2) .....................................193

Purpose of the present study........................................................................195

viii

Materials and Methods............................................................................196

Chemicals and reagents..............................................................................196

Preparation of matrix-matched standard.....................................................196

Instrumental conditions of GC-MS/MS......................................................197

Multiple reaction monitoring (MRM) profile optimization........................198

Sample preparation of pepper, orange, brown rice, and soybean................198

Defatting procedure in soybean using n-hexane/acetonitrile partitioning...199

Method validation......................................................................................199

Results and Discussion.............................................................................201

MRM optimization and selection of pesticides to be validated...................201

Characteristics of 384 pesticides.................................................................202

Comparison of the preparation procedures with/without n-hexane/

acetonitrile partitioning..............................................................................203

Method limit of quantitation (MLOQ)........................................................206

Instrumental repeatability...........................................................................209

Linearity of calibration...............................................................................210

Recovery.....................................................................................................215

Matrix effect...............................................................................................224

Conclusions...............................................................................................225

ix

Supplementary Information.......................................................................228

References.....................................................................................................255

초록...............................................................................................................273

x

List of Tables

Table 1. Representative pesticide analytical methods in biological

samples..........................................................................................7

Table 2. List of the 379 pesticides classified by chemical groups for the

optimized analytical method in serum......................................28

Table 3. List of representative chemical groups and 380 pesticides

selected for the final method validation in urine......................30

Table 4. The number of pesticides with recoveries between 70-120%

with RSDs below 20% in the recovery test from different

extraction methods for 379 Pesticides in 100 μL of human

serum (fortification Level at 250 ng/mL, n = 3)........................36

Table 5. The number of pesticides with recoveries between 70-120%

with RSDs below 20% in the recovery test from different

extraction methods for 379 Pesticides in 100 μL of human urine

(fortification Level at 250 ng/mL, n = 3)....................................37

Table 6. Distribution of linear ranges for 379 pesticides in serum for the

final established analytical method...........................................46

Table 7. Distribution of correlation coefficients (r2) for 379 pesticides in

serum for the final established analytical method....................47

Table 8. Distribution of linear ranges for 380 pesticides in urine for the


xi


urine for the final established analytical method.....................49

Table 10. Distribution of recovery and RSD range for 379 pesticides at

fortification levels of 10, 50, and 250 ng/mL in serum for the


Table 11. Pesticides for which recovery test results were not within 70-

120% (RSD ≤20%) at all treated levels (10, 50, and 250 ng/mL),

and intra-day accuracy results with RSD (serum)...................70


fortification levels of 10, 50, and 250 ng/mL in urine for the


Table 13. List of pesticides to be studied and their chemical groups.......97

Table 14. The optimized GC-MS/MS parameters including retention

times (tR), MRM transitions for each pesticide.......................100

Table 15. Representative pesticide multiresidue analytical method in

apiculture samples....................................................................124

Table 16. Sampling results in Giran during investigation period on April

24 to June 6, 2014......................................................................131

Table 17. Sampling results in Yeongyang during investigation period on

July 6 to August 7, 2014............................................................132

Table 18. The numbers of dead and healthy imago collected in the two

areas and their total and average body weights......................140

xii

Table 19. The established retention times (tR), monoisotopic masses,

quasi-molecular ion types, and MRM transitions of LC-

MS/MS for the neonicotinoid pesticides..................................142

Table 20. The limit of quantitation (LOQ), correlation coefficients (r2),

recovery results for neonicotinoid pesticides in bee, pollen, and

honey samples...........................................................................144

Table 21. Distribution of neonicotinoid residues in dead imago at three

sites in Giran.............................................................................147

Table 22. Distribution of neonicotinoid residues in dead imago at two

sites in Yeongyang.....................................................................148

Table 23. Distribution of neonicotinoid residues in pollen at two sites in

Giran.........................................................................................155

Table 24. Distribution of neonicotinoid residues in pollen at two sites in

Yeongyang.................................................................................156

Table 25. Distribution of neonicotinoid residues in honey in Giran and

Yeongyang.................................................................................163

Table 26. Positive detection frequency for bee, pollen, and honey samples

in Giran.....................................................................................166

Table 27. Distribution of median values and residue ranges for pesticide

multiresidues in Giran..............................................................170

Table 28. Positive detection frequency for bee, pollen, and honey samples

in Yeongyang.............................................................................174

xiii

Table 29. Distribution of median values and residue ranges for pesticide

multiresidues in Yeongyang.....................................................178

Table 30. The current pesticide regulation in crops and PLS to be

introduced in the Republic of Korea (Ministry of Food and

Drug Safety)..............................................................................185

Table 31. Review of tandem mass spectrometry for pest icide

multiresidues in agricultural products during three-year

publication (2016-2018)............................................................188

Table 32. Representative analytical methods for pesticide multiresidues

i n c l u d i n g s o l i d - p h a s e e x t r a c t i o n ( S P E ) c l e a n u p

procedure..................................................................................194

Table 33. Distribution of MLOQs for 384 pesticides in pepper, orange,

brown rice, and soybean...........................................................208

Table 34. Summary of instrumental repeatability to show distribution of

RSD of area for 384 pesticides in pepper, orange, brown rice,

and soybean (n = 7)...................................................................211


pepper, orange, brown rice, and soybean................................214

Table 36. Distribution of recoveries for 384 pesticides in pepper, orange,

brown rice, and soybean...........................................................216

xiv

List of Figures

Fig. 1. Scatter plot to show retention time (tR) and partition-coefficient

(log P) for 359 among 383 pesticides..............................................22

Fig. 2. Comparison of log P values calculated using the 1st order

regression model and values calculated in other works...............24

Fig. 3. TIC obtained by LC-MS/MS analysis of (a) matrix-matched

standard in human serum with 379 pesticides at 100 ng/mL (4 μL

injection) and (b) TIC of control (non-fortified) serum

sample..............................................................................................32

Fig. 4. TIC obtained by LC-MS/MS analysis of (a) matrix-matched

standard in human urine with 380 pesticides at 100 ng/mL (4 μL

injection) and (b) TIC of control (non-fortified) urine

sample..............................................................................................34

Fig. 5. Pie chart showing distribution of LOQs (ng/mL) for 379

pesticides in serum for the final optimized analytical method.

Light gray bar, 10 ng/mL; gray bar, 25 ng/mL; dark gray bar, 50

ng/mL; black bar, 100 ng/mL.........................................................40

Fig. 6. Pie chart showing distribution of LOQs (ng/mL) for 380

pesticides in urine for the final optimized analytical method.

Light gray bar, 10 ng/mL; gray bar, 25 ng/mL; dark gray bar, 50

ng/mL; black bar, 100 ng/mL.........................................................42

xv

Fig. 7. Scatter plots for 379 pesticides in serum to show accuracies and

precisions (RSD) in (a) intra-day and (b) inter-day tests (at 150

ng/mL of QC level)..........................................................................52

Fig. 8. Percentage of 379 pesticides satisfying the accuracy values within

80-120% (RSD ≤20%) at 10 ng/mL and within 85-115% (RSD

≤15%) at 50, 150, and 250 ng/mL in the intra-day (grey bars) and

inter-day (dark grey bars) tests using the final established method

in serum sample..............................................................................56

Fig. 9. Scatter plots for 380 pesticides in urine to show accuracies and

precisions (RSD) in (a) intra-day and (b) inter-day tests (at 150

ng/mL of QC level)..........................................................................60

Fig. 10. The number of pesticides satisfying the accuracy range of 80-120%

with RSD ≤20% at a QC level of 10 ng/mL and the accuracy

range of 85-115 with RSD ≤ 15% at 50, 150, and 250 ng/mL levels

under intra-day (grey bars) and inter-day (dark grey bars)

conditions in urine sample.............................................................62

Fig. 11. Distribution to show recovery values for 379 pesticides classified

into the representative chemical groups (treated at 50 ng/mL in

serum)..............................................................................................66

Fig. 12. Recovery results (treated at 250 ng/mL in serum) of three

different QuEChERS extraction methods for pH-dependent

pesticides that showed lower recovery rate in the validation

test....................................................................................................72

xvi

Fig. 13. Distribution of recovery rates for 380 pesticides by representative

chemical groups at fortification levels of 50 ng/mL in

urine................................................................................................76

Fig. 14. Scatter plot to show tR and matrix effect of 379 pesticides in

serum...............................................................................................80

Fig. 15. Distribution of matrix effects (%) for 379 pesticides classified

into soft effect (light grey bars, -20% to 0% and 0% to 20%),

middle effect (grey bars, -50% to -20% and 20% to 50%), and

strong effect (dark grey bars, <-50% and >50%) in human serum

samples............................................................................................84

Fig. 16. Scatter plot between retention time (tR) and matrix effect for 380

target pesticides in urine................................................................86

Fig. 17. Summary of matrix effects for 380 pesticides classified into soft

effect (light grey bars, -20% to 0% and 0% to 20%), middle effect

(grey bars, -50% to -20% and 20% to 50%), and strong effect

(dark grey bars, <-50% and >50%) in human urine

samples............................................................................................88

Fig. 18. Structures for phthalimide organochlorines, (a) captafol, (b)

captan, and (c) folpet. MRM chromatograms for matrix-matched

standards of (d) captafol, (e) captan, (f) folpet, and (g)-(i) these

recovery samples in serum, and MRM chromatograms for

matrix-matched standards of (j) captafol, (k) captan, (l) folpet,

and (m)-(o) these recovery samples in urine...............................104

xvii

Fig. 19. MRM chromatograms of (a) solvent-only standard, (b) matrix-

matched standard in serum, and (c) matrix-matched standard in

urine for binapacryl.....................................................................106

Fig. 20. Individual LOQs and correlation coefficients (r2) of 55 pesticides

for the final established analytical method in serum and

urine..............................................................................................108


with RSD ≤20% at a QC level of 10 ng/mL and the accuracy

range of 85-115 with RSD ≤ 15% at QC levels of 50, 150, and 250

ng/mL in (a) serum and (b) urine under intra-day (grey bars) and

inter-day (dark grey bars) conditions..........................................112

Fig. 22. Distribution of matrix effects for 380 pesticides in (a) serum and

(b) urine. The matrix effect was classified into soft effect (light

grey bars, -20% to 0% and 0% to 20%), middle effect (grey bars,

-50% to -20% and 20% to 50%), and strong effect (dark grey

bars, <-50% and >50%)...............................................................116

Fig. 23. Distribution of monitoring sites in the Republic of Korea.........128

Fig. 24. Distribution of residues for (a) clothianidin, (b) imidacloprid, and

(c) thiamethoxam in dead imago samples at three sites in

Giran.............................................................................................150


(c) thiamethoxam in dead imago samples at two sites in

Yeongyang.....................................................................................152

xviii


(c) thiamethoxam in pollen samples at three si tes in

Giran.............................................................................................158


(c ) thiamethoxam in pol len samples at two s i tes in

Yeongyang.....................................................................................160

Fig. 28. Distribution of the numbers of detection frequencies for

fluvalinate, etofenprox, carbaryl, acetamiprid, and spiromesifen,

which ranked first to fifth among the pesticide multiresidues by

the detection frequency................................................................168

Fig. 29. Distribution of the numbers of detection frequencies for

fluvalinate, etofenprox, acephate, etofenprox, flubendiamide, and

flonicamid, which ranked first to fifth among the pesticide

multiresidues by the detection frequency....................................176

Fig. 30. Scan chromatograms (m/z 50-500) for control soybean samples

of (a) partitioned and (b) non-partitioned procedures...............204

Fig. 31. Relative peak area (100 at 1st injection) of DDT-p,p', fenfuram,

folpet, methoxychlor (pepper), and chlorothalonil (orange) at 50

ng/mL to show peak decreases as the number of injections

increases........................................................................................212

Fig. 32. Percentages of pesticides satisfying recovery 70-120% (RSD

≤20%) classified by activity groups.............................................218

xix

Fig. 33. Percentages of pesticides satisfying recovery 70-120% (RSD

≤20%) classified by chemical groups at (a) 0.01 mg/kg and (b)

0.05 mg/kg.....................................................................................222

Fig. 34. Distribution of matrix effects for 384 pesticides in pepper, orange,

brown rice, and soybean. Group 3 and 4 are included in soft

matrix effect, Group 2 and 5 in medium effect, and Group 1 and

6 in strong effect............................................................................226

xx

List of Supplementary Information

Table S1. The retention times (tR), monoisotopic masses, quasi-

molecular ion types, and MRM transitions of LC-MS/MS for

the multiresidual pesticides...................................................228

Table S2. The optimized GC-MS/MS parameters including retention

times (tR), MRM transitions for each pesticide...................233

Table S3. List of general pesticide information for 384 pesticides.......239

1

Preface

Pesticides are used worldwide for the control of insects, microorganisms, fungi,

and other harmful pests in order to protect agricultural products. According to

a U.S. Environmental Protection Agency (EPA) report, world pesticide

expenditure at the producer level was $55,921 million in 2012 (Atwood and

Paisley-Jones, 2017). In the United States, $8,866 million was reported on the

same basis, corresponding to 16% of the world pesticide market (Atwood and

Paisley-Jones, 2017). In the Republic of Korea, the Korea Crop Protection

Association’s agrochemical book reported that the amount of pesticide

shipment was 19,798 tons in 2016 (Korea Crop Protection Association, 2017).

Use of pesticide has contributed to improving productivity, protection

of crop losses/yield reduction, and food quality (Aktar et al., 2009). Cooper and

Dobson (2007) reported that the use of pesticides has contributed to the

improvement of crop/livestock yields and quality, increased shelf life of

produce, and prevention of harmful organisms from interfering in human

activities and structures, from which secondary benefits such as national

agricultural economic development, reduced maintenance costs, or quality of

life improvement have followed (Cooper and Dobson, 2007).

Although pesticide has been a great influence on food security over the

decades, its toxicological/ecotoxicological effects on human and the ecosystem

also cannot be ignored. It is important to maintain pesticide residues below

sustainable levels in crops and the environment and to monitor in human,

environmental indicators, and food for safety management.

2

The purpose of the study is the analysis of pesticide multiresidues in

biological, apiculture samples, and representative crops. For the effective and

high-throughput multiresidue analysis, the scheduled multiple reaction

monitoring (MRM) mode of gas or liquid chromatography-tandem mass

spectrometry (GC-MS/MS or LC-MS/MS) was employed in every

methodology.

The study comprises three chapters. In Chapter I, novel bioanalytical

methods for multiresidual pesticides in serum and urine were developed using

LC-MS/MS (Part 1) after the comparison of three scaled-down QuEChERS

methods and validated with various parameters. These methodologies were

applied for GC-MS/MS amenable pesticides and the validation results in serum

and urine were discussed in Part 2. In Chapter II, modified QuEChERS

methods for neonicotinoids (clothianidin, imidacloprid, and thiamethoxam)

were validated in honey bee, pollen, and honey. With this analytical method and

multiresidue screening method, pesticide residues in apiculture samples were

determined and risk assessment was attempted for some pesticides in an aspect

of ecotoxicology. In the last chapter, Multiclass Pesticide Multiresidue Method

(No. 2) of the Korea Food Code was modified by scaling-down, and

approximately two hundreds of pesticides were newly verified with an original

GC-MS/MS list (about 200 pesticides) in the method (Chapter III). The

reinforced analytical method was validated and evaluated in four representative

crops (pepper, orange, brown rice, and soybean).

This pesticide multiresidue research provides a comprehensive

methodology for the residue determination in three major fields such as

forensic/clinical sciences, ecotoxicology, and agricultural/food chemistry.

3

Chapter I

Development and Validation of Pesticide

Multiresidue Analysis in Human Serum and Urine

Using LC-MS/MS and GC-MS/MS

4

Introduction

Pesticide intoxication

One of the major disadvantages of pesticides affected by human is that these

chemicals cause acute poisoning problems. Acute intoxication symptoms

caused by pesticides range from mild symptoms such as nausea, headache, and

paresthesia to fatalities (Thundiyil et al., 2008). Pesticide intoxication resulting

from intentional intake or misuse is a major social issue. Gunnell et al. (2007)

investigated the global distribution of suicide by pesticide and estimated that

there are 258,234 (plausible range from 233,997 to 325,907) suicides from

pesticide poisoning each year, representing 30% (27% to 37%) of all suicides

worldwide (Gunnell et al., 2007). In the United States, 234 deaths by pesticide

poisoning were identified over a 10 year span (1999 to 2008) according to the

Centers for Disease Control and Prevention’s Wide-ranging Online Data for

Epidemiologic Research (CDC WONDER) report, and an average of 20,116

people were exposed to pesticides annually, accounting for 17.8% of treatment

in healthcare facilities from 2006 to 2010 (Langley and Mort, 2012). In the

Republic of Korea, 16,161 reports of mortality and 45,291 reports of inpatient

and outpatient treatment related to pesticide intoxication were reported during

5 years (2006 to 2010) (Cha et al., 2014).

Various occupational researches have revealed that a large number of

farmers have experienced pesticide intoxication. Calvert et al. (2008)

investigated 3,271 cases of acute pesticide poisoning in the United States from

1998 to 2005 and reported that 2,334 (71%) were employed as farmworkers

(Calvert et al., 2008). It was reported that up to 25 million cases of pesticide

5

intoxication may be experienced by agricultural workers in the Asian

developing country (Jeyaratnam, 1990). In the Republic of Korea, it was

reported that 22.9% of 1,958 male farmers had experienced acute intoxication

symptoms within 48 h after using pesticides in 2010 (Kim et al., 2013). More

recently, Lee and coworkers in 2015 have surveyed 663 farmers in Gyeong-gi

province, South Korea, and 44 (6.63%) of them responded that they had

experienced acute poisoning within 24 h of spraying pesticide directly or

indirectly during 2013-2014 (Lee et al., 2015).

Pesticide analysis in biological samples

Biological monitoring of pesticide poisoning is useful for identifying evidence

of health problems in the environment/ecotoxicology, in the agricultural and

forensic fields, or for detoxification in a medical institution. Human biological

samples such as blood, urine, hair, and saliva have been primary sources for

determination of pesticides (Table 1).

Among the biological samples, blood is a regulated fluid, which means

that its volume does not vary substantially with water intake or other factors

(Barr et al., 2002). Therefore, blood is available without further dilutions for

determination of the internal concentration of pesticides. It is also advantageous

that pesticides are present in blood as parent compounds instead of their

metabolites as usually found in urine (Wessels et al., 2003), and blood has less

risk of exogenous or endogenous contamination compared to hair (Altshul et

al., 2004). Because only a few milliliters of blood from adults or less in the case

of children can be obtained, analytical methods for a few tens or several

hundreds of microliters of blood samples have been developed and validated to

6

overcome the sample volume problem (Mostafa et al., 2011; Saito et al., 2013;

Wittsiepe et al., 2014). Serum analysis is usually preferred over whole blood

analysis, because serum has a minor matrix complexity, and is a more

homogenous material (Gill et al., 1996; Hernández et al., 2002). Therefore, one

or more cleanup steps can be reduced with serum samples compared to whole

blood (Lacassie et al., 2001a; Hernández et al., 2002).

Urine also has several advantages over other samples. Urine is easier to

obtain than invasive samples such as blood, and larger amounts of urine are

available compared with blood, hair, and saliva. Because urine is a

homogeneous biological fluid composed of 95% water (Cortéjade et al., 2016),

complex preparation steps for purification of target pesticides are not needed.

Although most pesticides are metabolized rapidly in the body and excreted in

urine as free metabolites, mercapturate detoxification products, and/or

glucuronide or sulfate-bound compounds within 48 h (Hernández et al., 2005),

various chemical groups of pesticides still remain intact and present in urine

(Montesano et al., 2007; Usui et al., 2012; Quansah et al., 2016). It is easier and

less costly to obtain analytical standards of pesticides rather than those of

metabolites.

Screening of as many parent compounds as possible is also needed in

many applications because there have been deaths resulting from various

chemical groups of pesticides (Lee et al., 2010), some of them (e.g.,

benzoximate and etofenprox) showing very low acute toxicity (LD50 >10,000

mg/kg; oral acute for rat) (Turner, 2015).

7

Table 1. Representative pesticide analytical methods in biological samples

No. Matrix Instrument Sample preparation

Number of

analytes

Reference

1 Blood GC-MS LLE1) 11 (Papoutsis et al., 2012)

2 Saliva TD-ESI2)/MS LLE 5 (Lee et al., 2016)

3 Serum GC-MS/MS SPE3) 20 (Chang et al., 2016)

4 Blood LC-MS/MS, LC-(IT4))MS/Orbitrap,

and GC-MS

QuEChERS5) 64 (Plassmann et al., 2015)

5 Serum and urine LC-MS/MS SPE 3 (Watanabe et al., 2014)

6 Urine LC-MS/MS SPE 6 (Ueyama et al., 2014)

7 Blood LC-MS/MS and LC-MS/TOF6)

QuEChERS 215 (Kim et al., 2014)

8 Serum and urine LC-(ICP7))MS Dilute-and-shoot 4 (Kazui et al., 2014)

9 Urine LC-MS/MS LLE 1 (Garner and Jones, 2014)

10 Serum GC-MS/TOF SPE 50 (Fan et al., 2014)

11 Serum LC-MS/MS PP8) 29 (Dong et al., 2014)

12 Serum GC-MS CC9) 4 (Azandjeme et al., 2014)

8

Table 1. (Continued)


Number of

analytes

Reference

13 Serum LC-MS/MS Monolithic spin column

16 (Saito et al., 2013)

14 Hair and Urine LC-MS SLE10) and LLE 2 (Kavvalakis et al., 2013)

15 Blood and urine LC-MS/MS QuEChERS 6 (Usui et al., 2012)

16 Blood and urine GC-MS SPE 1 (Takayasu et al., 2012)

17 Hair GC-MS/MS SPME11) 50 (Schummer et al., 2012)

18 Serum and urine LC-FLD Dilute-and-shoot 2 (Esteve-Romero et al., 2012)

19 Serum and urine GC-MS Monolithic spin column

3 (Saito et al., 2011)

20 Plasma LC-MS/MS PP 3 (Mostafa et al., 2011)

21 Serum GC-MS SPME 2 (Kasiotis et al., 2011)

22 Urine GC-(IT)MS/MS and LC-MS/MS

SPE >200 (Cazorla-Reyes et al., 2011)

23 Urine LC-MS/MS LLE 6 (Montesano et al., 2007)

24 Blood GC-(IT)MS/MS SPME 11 (Hernández et al., 2002)

9



Number of

analytes

Reference

25 Serum and plasma GC-HR12)MS SPE 29 (Barr et al., 2002)

26 Blood, serum GC-MS SPE 29 (Lacassie et al., 2001a)

27 Serum LC-MS and GC-MS

SPE 61 (Lacassie et al., 2001b)

28 Serum and urine LC-MS/MS PP and Direct injection

2 (Sancho et al., 2000)

1)Liquid-liquid extraction 7)Inductively coupled plasma

2)Thermal desorption electrospray 8)Protein precipitation

3)Solid-phase extraction 9)Column chromatography

4)Ion-trap 10)Solid-liquid extraction

5)Quick, Easy, Cheap, Effective, Rugged, and Safe 11)Solid-phase microextraction

6)Time-of-flight 12)High resolution

10

Advantage of the tandem mass spectrometry

In the case of pesticide intoxication, analysis of multiresidue pesticides with

high reliability and speed is important in order to identify unknown compounds

for further medical treatment and forensic investigation. Traditional liquid

chromatography (LC) and gas chromatography (GC) have many limitations in

specificity, sensitivity, and speed for multiresidue analysis (Aysal et al., 2007;

Moliner-Martínez et al., 2011). Furthermore, both conventional instruments

require partitioning or cleanup procedures to remove interference, which takes

a long time, uses high volumes of solvents, and may remove target compounds

during extensive cleanup steps. A single quadrupole (SQ) mass filter overcomes

some problems by performing selected ion monitoring (SIM) but still can fail

to distinguish a target pesticide from other pesticides or interferences with a

similar retention time (tR) and m/z. Tandem mass spectrometry coupled with

liquid chromatography (LC) or gas chromatography (GC) has been widely

utilized. Among the tandem mass spectrometry, triple quadrupole (TQ)

analyzers is a powerful analytical technique for quantitative detection of a broad

range of pesticides in short time with simultaneous manner by operating in

multiple reaction monitoring (MRM) mode in biological monitoring.

Preparation methodology for biological sample

In various alternative cleanup procedures for serum and urine sample, column

chromatography (CC), solid-phase extraction (SPE), liquid-liquid extraction

(LLE) have been reported as representative preparation methods (Table 1).

Protein precipitation (PP) with acetonitrile solvent is specific for blood (serum)

sample due to its protein molecule (Sancho et al., 2000; Dong et al., 2014). CC

11

and SPE are advantageous for a few specific target compounds, but take much

time and effort to conduct and have some difficulty finding optimum

washing/elution conditions covering various chemical properties. LLE and PP

are more convenient than CC or SPE, but have similar drawbacks to CC or SPE

and interferences of serum or urine may remain in the extract to cause a serious

matrix effect or lead to low extraction efficiency.

The aqueous characteristic of urine and advanced separation techniques

such as LC- or GC-MS/MS make urine preparation relatively convenient and

easy. Direct injection or dilute-and-shoot procedures are the simplest ways to

identify pesticides in urine (Esteve-Romero et al., 2012; Kazui et al., 2014;

Cortéjade et al., 2016). Nevertheless, these processes have major problems in

that urinary salts or macromolecules may decrease the sensitivity of an

instrument or cause severe clogging on the injection syringe or ESI probe.

The QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe)

method is a preparation method with strong extraction efficiency and

convenience in multiresidue analysis. Since the first unbuffered QuEChERS

analytical method for crops was developed in 2003, a number of improved

QuEChERS methods have been validated and applied (Anastassiades et al.,

2003). Among these methods, AOAC 2007.01 and EN 15662 methods, in

which buffer reagents are contained for adjustment of sample pH, have been

used widely with advantages in extraction rate (recovery) of pH-dependent

pesticides (Lehotay, 2007; EN 15662, 2008). Recently, QuEChERS methods

have been used for biological samples in clinical and forensic toxicology (Usui

et al., 2012; Kim et al., 2014; Plassmann et al., 2015).

12

Purpose of the present study

In this study, a simultaneous multiresidue screening analytical method in

human serum and urine was developed and validated using LC- and GC-

MS/MS. The scheduled MRMs and retention times (tR) for each analyte were

optimized for qualification and quantitation within 15 minutes (LC-MS/MS)

and 30 minutes (GC-MS/MS) per sample. This chapter of the study is

comprised of two part. In Part 1, 379 pesticides in serum and 380 in urine were

investigated using LC-MS/MS. Three different versions of QuEChERS

extraction methods were compared and modified to use a very small sample

volume (100 μL) without using dispersive SPE (dSPE) in the cleanup procedure.

Using the established method in Part 1, 54 pesticide in serum and 55 in urine

were evaluated using GC-MS/MS in Part 2. It was found that acceptable

validation data (limit of quantitation (LOQ), linearity of calibration, accuracy

and precision, recovery, and matrix effect) for most pesticides were obtained.

This fast and convenient analytical method is applicable for biomonitoring of

pesticide multiresidues in serum and urine samples from food toxicology,

agricultural operator exposure, clinical and forensic studies and investigation.

13

Part 1



Using LC-MS/MS

14

Materials and Methods

Chemicals and reagents

Individual pesticide standards (purity >98%) or stock solutions (1,000 mg/L)

for quality control (QC) were obtained from ChemService (West Chester, PA),

Dr. Ehrenstorfer (Augsburg, Germany), Sigma-Aldrich (St, Louis, MO), Wako

Pure Chemical Industries (Osaka, Japan), and ULTRA Scientific (North

Kingstown, RI). Ammonium formate (≥99.0%), formic acid (LC-MS grade),

acetic acid (HOAc, ≥99.7%), magnesium sulfate anhydrous (MgSO4, ≥99.5%),

sodium acetate anhydrous (NaOAc, ≥99.0%), sodium citrate dibasic

sesquihydrate (Na2HCitr·1.5H2O, ≥99.0%), and sodium citrate tribasic

dihydrate (Na3Citrate·2H2O, ≥99.0%) were purchased from Sigma-Aldrich.

Sodium chloride (NaCl, 99.0%) was obtained from Samchun (Gyeonggi-do,

South Korea). Methanol and acetonitrile (HPLC grade) were purchased from

Fisher Scientific (Seoul, South Korea). Ceramic homogenizers (2 mm) were

purchased from Ultra Scientific. Deionized water was prepared in house using

LaboStar TWF UV 7 (Siemens, MA). Serum from human male was obtained

from Sigma-Aldrich. Human urine was collected from healthy volunteers with

the permission of the Institutional Review Board (IRB) at Seoul National

University, Seoul, the Republic of Korea. Samples were stored at -70 °C until

preparation and analysis.

Preparation of standard solutions

Individual pesticide stock solutions (1,000 mg/L) were prepared in acetonitrile.

For pesticides that were difficult to dissolve at this concentration level (e.g.,

15

carbendazim), acetone, methanol, or water were used instead of acetonitrile or

lower concentrations of stock solutions were prepared so that these components

could be sufficiently dissolved. To prepare four groups of intermediate mixed

stock solutions at 10 mg/L, a portion of each stock solution was brought up with

acetonitrile in a 25-mL volumetric flask. The aliquots of intermediates were

again mixed to make a final mixed standard solution at 2.5 mg/L. This was

diluted with acetonitrile to make the mixed working standard solutions of lower

concentrations for preparing calibration curves and using in several validation

procedures.

LC-MS/MS parameters

LC-MS/MS analysis was carried out on a Shimadzu Nexera X2 UHPLC

system coupled to a Shimadzu LCMS-8050 triple quadrupole mass

spectrometer (Kyoto, Japan). The UHPLC system comprised a solvent delivery

module (LC-30AD), column oven (CTO-20A), autosampler (SIL-30AC), and

degassing unit (DGU-20A5R). A Kinetex C18 column (100 × 2.1 mm, 2.6 µm,

Phenomenex, Torrance, CA) was used for analyte separation, and a

SecurityGuard Ultra guard column (Phenomenex) was connected to the column

to prevent contamination. The oven temperature was maintained at 40 °C. The

total flow rate of the mobile phase was 0.2 mL/min. For the mobile phases,

solvent A was 5 mM ammonium formate and 0.1% formic acid in water and B

was 5 mM ammonium formate and 0.1% formic acid in methanol. For the

gradient program, mobile phase B was initialized at 5%, and after 0.5 minutes,

B was raised to 55% for 0.5 min, ramped to 95% for 7 min, held for 3 min,

raised to 100% for 1 min, then dropped sharply to 5% for 0.1 min, and held for

16

2.9 min. The total analytical time was 15.0 min, and the injection volume was

4 µL. LabSolutions software (version 5.72) was used for multiresidue MRM

data processing.

In the mass spectrometer system, ionization of target analytes was

performed by a heated electrospray ionization (ESI) with positive/negative

switching mode. The interface, desolvation line (DL), and heat block

temperature were 300, 250, and 400 °C, respectively. The heating gas (air),

nebulizing (nitrogen), and drying gas (nitrogen) flow were 10, 3, and 15 L/min,

respectively. The collision-induced dissociation (CID) gas was argon. For

MS/MS analysis, each standard solution (0.1-1 mg/L) was injected without the

column to obtain a full scan spectrum (m/z 50-500 or m/z 100-1,000). A

precursor ion (e.g., [M+H]+) was selected from the spectrum data and subjected

to collision with several collision energy (CE) voltages to find the two product

ions that showed the highest and the second highest detection intensity. The

former ion transition was used as a quantifier for quantitation of the target

compound, and the latter was used as a qualifier for its reference. This MRM

was scheduled by the retention time of each compound, and the MRM detection

window was ±0.5 min. Finally, dwell times (≥ 2.0 ms) were adjusted

automatically based upon loop time (0.12 s) for the maximized data acquisition.

LabSolutions (version 5.72) as LCMS software was utilized for data processing.

Comparison of three versions of QuEChERS

Human serum and urine (0.1 mL) were extracted with three different

QuEChERS extraction reagents scaled-down as follows: (A) original

QuEChERS (Anastassiades et al., 2003) procedure (0.4 mL of acetonitrile, 40

17

mg of MgSO4, and 10 mg of NaCl); (B) QuEChERS of AOAC 2007.01

(Lehotay, 2007) procedure (1% HOAc in acetonitrile (0.4 mL), 40 mg of

MgSO4, and 10 mg of NaOAc); and (C) QuEChERS of EN 15662 (EN 15662,

2008) procedure (0.4 mL of acetonitrile, 40 mg of MgSO4, 10 mg of NaCl, 10

mg of Na3Citrate·2H2O, and 5 mg of Na2HCitr·1.5H2O). The extract from each

method was centrifuged and 0.2 mL of supernatants were mixed with 0.05 mL

of acetonitrile for matrix-matching. Each of serum and urine sample was

equivalent to 0.2 mL per mL of final extract. Finally, 4 µL of the sample was

analyzed by LC-MS/MS.

Final established sample preparation

Each serum and urine (0.1 mL) sample in a 2-mL microcentrifuge tube was

extracted respectively, with 0.4 mL of acetonitrile by shaking for 1 min at 1,200

rpm using a Geno Grinder (1600 MiniG SPEX Sample Prep, Metuchen, NJ).

Forty milligrams of MgSO4 and 10 mg of NaCl were added under ice bath

conditions to prevent heat caused by MgSO4. The tube was centrifuged for 5

min at 13,000 rpm using microcentrifuge (17TR, Hanil Science, Seoul, the

Republic of Korea). The supernatant (0.2 mL) was transferred into a 2-mL

amber glass vial and mixed with 0.05 mL of acetonitrile for matrix-matching.

Without further cleanup steps, 4 µL of the final extraction sample was taken

into LC-MS/MS for analysis of target analytes.

Validation of analytical methods

For determination of the LOQ and linearity of calibration, matrix-matched

procedure standard solutions at 10, 20, 50, 100, 150, and 250 ng/mL were

18

analyzed. The minimum concentration satisfying a signal to noise ratio (S/N)

greater than 10 on the chromatogram was selected as the LOQ.

The linearity of calibration was evaluated (n = 5) by the correlation

coefficient (r2) of the calibration curve from 10 to 250 ng/mL. The r was

calculated using the following equation (Almeida et al., 2002):

𝑟𝑟 =∑𝑤𝑤𝑖𝑖 ∙ ∑𝑤𝑤𝑖𝑖𝑥𝑥𝑖𝑖 𝑦𝑦𝑖𝑖 − ∑𝑤𝑤𝑖𝑖𝑥𝑥𝑖𝑖 ∙ ∑𝑤𝑤𝑖𝑖𝑦𝑦𝑖𝑖

�∑𝑤𝑤𝑖𝑖 ∙ ∑𝑤𝑤𝑖𝑖𝑥𝑥𝑖𝑖2 −(∑𝑤𝑤𝑖𝑖𝑥𝑥𝑖𝑖)2 ∙ �∑𝑤𝑤𝑖𝑖 ∙ ∑𝑤𝑤𝑖𝑖𝑦𝑦𝑖𝑖2 −(∑𝑤𝑤𝑖𝑖𝑦𝑦𝑖𝑖)2

where: 𝑤𝑤𝑖𝑖 = a weighting regression factor

𝑥𝑥𝑖𝑖 𝑎𝑎𝑎𝑎𝑎𝑎 𝑦𝑦𝑖𝑖 = 𝑖𝑖th data pair of 𝑎𝑎 total data pairs

A weighting regression factor of 1/x (wi = 1/xi) was adopted to minimize

calculation error at low concentrations. By using the weighting method, a 1st

order linear regression model (y = a + bx) from the least squares approximation

was converted adding weighting factor wi (Almeida et al., 2002).

𝑏𝑏 =∑𝑤𝑤𝑖𝑖 ∙ ∑𝑤𝑤𝑖𝑖𝑥𝑥𝑖𝑖 𝑦𝑦𝑖𝑖 − ∑𝑤𝑤𝑖𝑖𝑥𝑥𝑖𝑖 ∙ ∑𝑤𝑤𝑖𝑖𝑦𝑦𝑖𝑖

∑𝑤𝑤𝑖𝑖 ∙ ∑𝑤𝑤𝑖𝑖𝑥𝑥𝑖𝑖2 −(∑𝑤𝑤𝑖𝑖𝑥𝑥𝑖𝑖)2

𝑎𝑎 =∑𝑤𝑤𝑖𝑖𝑥𝑥𝑖𝑖2 ∙ ∑𝑤𝑤𝑖𝑖𝑦𝑦𝑖𝑖 − ∑𝑤𝑤𝑖𝑖𝑥𝑥𝑖𝑖 ∙ ∑𝑤𝑤𝑖𝑖𝑥𝑥𝑖𝑖 𝑦𝑦𝑖𝑖

∑𝑤𝑤𝑖𝑖 ∙ ∑𝑤𝑤𝑖𝑖𝑥𝑥𝑖𝑖2 −(∑𝑤𝑤𝑖𝑖𝑥𝑥𝑖𝑖)2

where: 𝑏𝑏 = slope of the regression equation

𝑎𝑎 = 𝑦𝑦 intercept of the regression equation

Accuracy and precision tests were performed using a QC sample (a sample with

a known quantity of analyte (US FDA, 2013)) at 10, 50, 150, and 250 ng/mL

levels. The intra-day tests were conducted by analyzing five replicates of each

19

treated level in a single day. The inter-day tests were carried out by analyzing

one QC sample of each treated level per day for five separate days.

To verify the extraction efficiency of the preparation process, recovery

tests at fortification levels of 10, 50, and 250 ng/mL were conducted. Five µL

of mixed working standard solutions (200, 1,000, and 5,000 ng/mL) in

acetonitrile were fortified in 0.1 mL of each blank serum or urine, respectively

and the treated samples were prepared as the final established preparation

procedures (n = 3). Recovery of target compounds was determined using

calibration curves of matrix-matched standards to compensate for matrix effects

in LC-MS/MS analysis.

The matrix effect was also calculated by comparing the slope of the

calibration curve of the matrix-matched standards with that of the calibration

curve of the solvent-based standards using the following equation:

Matrix effect, % = �Slope of matrix-matched standard calibration

Slope of solvent-based standard calibration− 1� × 100

Safety information

All pesticide standards and reagents used in this study were handled according

to the Material Safety Data Sheet (MSDS)’s safety instructions. For all

instrumentation, the manufacturer's safety information was followed and

implemented.

20

Results and Discussion

Optimization of multiple reaction monitoring (MRM) in LC-MS/MS

For the determination of MRM transition profiles, a full scan analysis was first

performed with 400 pesticides. In this step, 17 compounds (binapacryl,

bromophos-methyl, chlorpropham, cyanophos, cyfluthrin, dichlofluanid,

dicofol, disulfoton, endosulfan-sulfate, ethalfluralin, isofenphos, isofenphos-

methyl, nitrothal-isopropyl, oxyfluorfen, parathion-methyl, silafluofen, and

spiromesifen) did not give a suitable quasi-molecular ion (precursor ion) and

were excluded. These compounds were analyzed using GC-MS/MS in Part 2.

The remaining 383 components successfully produced precursor ions. Among

them, 326 target compounds were [M+H]+ quasi-molecular ion form, 24

compounds were [M+NH4]+ form, seven compounds (abamectin B1a,

alanycarb, aldicarb, butocarboxim, lepimectin A3, lepimectin A4, and

pyribenzoxim) were [M+Na]+ form, and two compounds (milbemectin A3 and

milbemectin A4) were [M+H-H2O]+ form in the ESI positive mode. Twenty

three pesticides were [M-H]- form and dithianon showed an ion form [M·]- in

the ESI negative mode. After CID step, quantifier and qualifier ions were

selected depending on intensity. After MRM optimization steps, retention time

and sensitivity of target compounds were verified using both the solvent-based

standards (acetonitrile) and matrix-matched standard of serum and urine.

However, folpet was rejected after this step due to its poor response in all

standard types (further discussed in Part 2).

21

Relationship between partition-coefficient and retention time

The partition-coefficient, abbreviated P, is the ratio of the concentration of a

compound in liquid A and B when the two-layer solution is equilibrium at a

constant pH and temperature. Usually, un-ionized water and octanol are used

as liquid A and B. In this case, P value is a parameter of hydrophobicity. The P

value is expressed as the logarithm and calculated using the following equation:

log𝑃𝑃 = log([𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠]𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜[𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠]𝑤𝑤𝑜𝑜𝑜𝑜𝑡𝑡𝑡𝑡

)

There have been attempts to measure log P from the retention times (tR)

of various compounds using HPLC (Valko et al., 2001). In this study, log P and

retention time (tR) established from MRM profiles were investigated and the

relationship between the two parameters was verified. Among the 383

pesticides, log P values for 359 compounds were found (MacBean, 2012;

Turner, 2015). The correlation coefficient (r, unweighted) value between tR and

log P was +0.8737 (Fig. 1). The results showed that tR and log P have strong

positive correlations. Using the 1st order regression model (y = 0.8701x -

2.3548), log P values for the remaining 24 pesticides that have no log P data

were predicted (Fig. 2). The results were compared to the calculated log P data

from other works (IUPAC; Chemicalize.org, 2017). The results showed that the

log P values of most pesticides from the two data sources were similar. Unlike

these compounds, cartap exhibited a significant difference (>3) between the two

data. In order to establish a more elaborate model to explain well between the

two variables, more information on pesticides such as pKa is needed in addition

to log P.

22

Fig. 1. Scatter plot to show retention time (tR) and partition-coefficient (log

P) for 359 among 383 pesticides

23

y = 0.8701x - 2.3548-4

-2

0

2

4

6

8

10

0 3 6 9 12

log

P

Retention time (min)

24

Fig. 2. Comparison of log P values calculated using the 1st order regression

model and values calculated in other works

25

-1.00.01.02.03.04.05.06.07.0

Cal

cula

ted

log

P

Pesticide

Value calculated by regression model Referenced value (calculated)

26

Optimization of sample extraction step

Serum and urine are liquid-based samples, so it is appropriate to prepare the

sample using QuEChERS methods for high extraction efficiency (recovery) of

multiresidual pesticides. Since the first QuEChERS method for crops was

developed using GC-MS in 2003 (Anastassiades et al., 2003), there have been

preparation procedure improvements for LC amenable pesticides or lower

recovery rate compounds such as pH-dependent pesticides (Koesukwiwat et al.,

2008; Ribeiro Begnini Konatu et al., 2017). The official procedures such as the

AOAC 2007.01 method containing acetate buffers and the EN 15662 method

containing citrate buffers have been developed to improve recovery efficiency

for pH-dependent pesticides (Lehotay, 2007; EN 15662, 2008). The entire or a

portion of these types of QuEChERS methods have been utilized or modified

depending on the characteristics of the pesticides and sample matrices in many

analytical studies (Rejczak and Tuzimski, 2015).

In this study, the sample size was reduced to 0.1 mL, and optimization

of the final preparation step was established by comparing the scaled-down

methods from three different QuEChERS procedures (Anastassiades et al.,

2003; Lehotay, 2007; EN 15662, 2008).

For the serum sample, bensultap, dithianon, and the acidic flonicamid

metabolite TFNG [N-(4-trifluoromethylnicotinoyl)glycine] were again rejected

because they could not be recovered. Except for the rejected three analytes

(bensultap, dithianon, and TFNG), the remaining 379 pesticides were selected

as the final research analytes in serum (Table 2). For the urine sample, aldicarb

and bensultap were not recovered at all in any of the three preparation methods.

27

Therefore, these two compounds were excluded, and the remaining 380

pesticides were selected for final validation in urine (Table 3).

The total ion chromatogram (TIC) for the 379 target analytes in serum

and the 380 analytes urine sample is shown in Fig. 3 and 4. There were no false

positives in non-fortified serum samples, and no overlaps were observed

between pesticides in fortified samples.

The number of pesticides that satisfied the recovery range from 70 to

120% with relative standard deviation (RSD) below 20% based on the criteria

of SANTE/11813/2017 (European Commission, 2017) and their percentage

ratio for each extraction method in serum and urine are shown in Table 4 and

5. There was no significant difference in the number of analytes satisfying the

recovery criteria between the three methods in both matrices. For the serum

sample, 344 (90.8%), 341 (90.0%), and 341 (90.0%) of the total 379 pesticides

satisfied the recovery criteria for methods (A), (B), and (C), respectively.

Method (A), the unbuffered condition, showed slightly higher number of

pesticides than the others. For the urine sample, 360 (94.7%), 359 (94.5%), and

357 (93.9%) of total pesticides for methods (A), (B), and (C) fell within the

criteria, respectively. From the optimization experiment results, the final

preparation method using method (A) (downsized original QuEChERS) was

established in both matrices. In addition, further cleanup steps, such as dSPE,

were discarded in this treatment method to prevent the loss of labile target

analytes and minimize the analysis time.

28

Table 2. List of the 379 pesticides classified by chemical groups for the

optimized analytical method in serum

Chemical group (No. of compounds)

Compound name

Aryloxyalkanoic/ Aryloxyphenoxypropionic acid

(12)

2,4-D, Clomeprop, Cyhalofop-butyl, Diclofop-methyl, Fenoxaprop-p-ethyl, Haloxyfop, Haloxyfop-R-Methyl, MCPA, Mecoprop-P, Metamifop, Propaquizafop, Quizalofop-ethyl

Avermectin/ Spinosyn

(11)

Abamectin B1a, Emamectin B1a, Emamectin B1b, Lepimectin A3, Lepimectin A4, Milbemectin A3, Milbemectin A4, Spinetoram (XDE-175-J), Spinetoram (XDE-175-L), Spinosyn A, Spinosyn D

Carbamate (42)

Alanycarb, Aldicarb, Asulam, Bendiocarb, Benfuracarb, Benthiavalicarb-isopropyl, Butocarboxim, Carbaryl, Carbofuran, Carbosulfan, Cycloate, Dazomet, Di-allate, Diethofencarb, Dimepiperate, Esprocarb, Ethiofencarb, Fenobucarb (BPMC), Fenothiocarb, Fenoxycarb, Furathiocarb, Iprovalicarb, Isoprocarb, Methiocarb, Methomyl, Metolcarb, Molinate, Oxamyl, Pebulate, Phenmedipham, Pirimicarb, Promecarb, Propamocarb, Propham, Propoxur, Pyributicarb, Thiobencarb, Thiodicarb, Tri-allate, Trimethacarb, Vernolate, XMC

Imidazolinone (5)

Fenamidone, Imazamox, Imazapic, Imazaquin, Imazethapyr

Neonicotinoid (7)

Acetamiprid, Clothianidin, Dinotefuran, Imidacloprid, Nitenpyram, Thiacloprid, Thiamethoxam

Organophosphate (64)

Acephate, Anilofos, Azamethiphos, Azinphos-ethyl, Azinphos-methyl, Bensulide, Cadusafos, Carbophenothion, Chlorfenvinphos, Chlorpyrifos, Chlorpyrifos-methyl, Demeton-S-methyl, Diazinon, Dichlorvos, Dicrotophos, Dimethoate, Dimethylvinphos, Edifenphos, EPN, Ethion, Ethoprophos, Etrimfos, Fenamiphos, Fenthion, Fonofos, Fosthiazate, Imicyafos, Iprobenfos, Isazofos, Isoxathion, Malathion, Mecarbam, Methamidophos, Methidathion, Mevinphos, Monocrotophos, Omethoate, Oxydemeton-methyl, Parathion, Phenthoate, Phorate, Phosalone, Phosmet, Phosphamidon, Phoxim, Piperophos, Pirimiphos-ethyl, Pirimiph;os-methyl, Profenofos, Prothiofos, Pyraclofos, Pyrazophos, Pyridaphenthion, Quinalphos, Sulprofos, Tebupirimfos, Terbufos, Tetrachlorvinphos, Thiometon, Tolclofos-methyl, Triazophos, Tribufos, Trichlorfon, Vamidothion

Pyrethroid (14)

Bifenthrin, Cycloprothrin, Cyhalothrin-lambda, Cypermethrin, Deltamethrin, Etofenprox, Fenpropathrin, Fenvalerate, Flucythrinate, Fluvalinate, Halfenprox, Permethrin, Phenothrin, Tralomethrin

Strobilurin (8)

Azoxystrobin, Fluacrypyrim, Kresoxim-methyl, Metominostrobin, Orysastrobin, Picoxystrobin, Pyraclostrobin, Trifloxystrobin

Triazine (12)

Ametryn, Atrazine, Cyanazine, Dimethametryn, Hexazinone, Metribuzin, Prometryn, Propazine, Simazine, Simetryn, Terbuthylazine, Terbutryn

Triazole (26)

Amisulbrom, Azaconazole, Bitertanol, Cafenstrole, Carfentrazone-ethyl, Cyproconazole, Difenoconazole, Diniconazole, Epoxiconazole, Fenbuconazole, Fluquinconazole, Flusilazole, Hexaconazole, Imibenconazole, Metconazole, Myclobutanil, Paclobutrazol, Penconazole, Propiconazole, Simeconazole, Tebuconazole, Tetraconazole, Triadimefon, Triadimenol, Triticonazole, Uniconazole

29



Compound Name

Urea (34)

Azimsulfuron, Bensulfuron-methyl, Chlorfluazuron, Chlorimuron-ethyl, Chlorotoluron, Chlorsulfuron, Cyclosulfamuron, Daimuron, Diafenthiuron, Diflubenzuron, Diuron, Ethametsulfuron-methyl, Ethoxysulfuron, Flucetosulfuron, Flufenoxuron, Forchlorfenuron, Halosulfuron-methyl, Hexaflumuron, Imazosulfuron, Isoproturon, Linuron, Lufenuron, Metazosulfuron, Methabenzthiazuron, Metobromuron, Nicosulfuron, Novaluron, Pencycuron, Rimsulfuron, Teflubenzuron, Thidiazuron, Thifensulfuron-methyl, Tribenuron-methyl, Triflumuron

Others/ Unclassified

(144)

Acibenzolar-S-methyl, Alachlor, Allidochlor, Ametoctradin, Amitraz, Benfuresate, Bentazone, Benzobicyclon, Benzoximate, Bifenazate, Bifenox, Boscalid, Bromacil, Bromobutide, Bromoxynil, Bupirimate, Buprofezin, Butachlor, Butafenacil, Carbendazim, Carboxin, Carpropamid, Cartap, Chinomethionat, Chlorantraniliprole, Chloridazon, Chromafenozide, Cinmethylin, Clethodim, Clofentezine, Clomazone, Cyazofamid, Cyflufenamid, Cymoxanil, Cyprodinil, Cyromazine, Diflufenican, Dimethachlor, Dimethenamid, Dimethomorph, Diphenamid, Diphenylamine, Dithiopyr, Ethaboxam (EBX), Ethoxyquin, Etoxazole, Famoxadone, Fenarimol, Fenazaquin, Fenhexamid, Fenoxanil, Fenpyroximate, Fentrazamide, Ferimzone, Fipronil, Flonicamid, Fluazinam, Flubendiamide, Fludioxonil, Flufenacet, Flumiclorac-pentyl, Flumioxazin, Fluopicolide, Fluopyram, Flusulfamide, Flutolanil, Fluxapyroxad, Hexythiazox, Imazalil, Inabenfide, Indanofan, Indoxacarb, Iprodione, Isoprothiolane, Isopyrazam, Lactofen, Mandipropamid, Mefenacet, Mefenpyr-diethyl, Mepanipyrim, Mepronil, Metalaxyl, Methoxyfenozide, Metolachlor, Metrafenone, Napropamide, Nitrapyrin, Nuarimol, Ofurace, Oxadiazon, Oxadixyl, Oxaziclomefone, Pendimethalin, Penoxsulam, Penthiopyrad, Picolinafen, Pretilachlor, Probenazole, Prochloraz, Propachlor, Propanil, Propisochlor, Propyzamide, Pymetrozine, Pyrazolynate, Pyrazoxyfen, Pyribenzoxim, Pyridaben, Pyridalyl, Pyridate, Pyrifenox, Pyrifluquinazon, Pyrimethanil, Pyrimidifen, Pyriminobac-methyl E, Pyriminobac-methyl Z, Pyrimisulfan, Pyriproxyfen, Pyroquilon, Quinmerac, Quinoclamine, Saflufenacil, Sethoxydim, Spirodiclofen, Spirotetramat, Sulfoxaflor, TCMTB, Tebufenozide, Tebufenpyrad, TFNA [4-trifluoromethyl nicotinic acid], Thenylchlor, Thiabendazole, Thiazopyr, Thifluzamide, Thiocyclam, Thiophanate-methyl, Tiadinil, Tolfenpyrad, Tolylfluanid, Triclopyr, Tricyclazole, Triflumizole, Trifluralin, Zoxamide

30

Table 3. List of representative chemical groups and 380 pesticides selected for

the final method validation in urine


Compound name

Aryloxyalkanoic/ Aryloxyphenoxy-propionic acid

(12)

2,4-D, Clomeprop, Cyhalofop-butyl, Diclofop-methyl, Fenoxaprop-p-ethyl, Haloxyfop, Haloxyfop-R-Methyl, MCPA, Mecoprop-P, Metamifop, Propaquizafop, Quizalofop-ethyl

Avermectin/ Spinosyn

(11)

Abamectin B1a, Emamectin B1a, Emamectin B1b, Lepimectin A3, Lepimectin A4, Milbemectin A3, Milbemectin A4, Spinetoram (XDE-175-J), Spinetoram (XDE-175-L), Spinosyn A, Spinosyn D

Carbamate (41)

Alanycarb, Asulam, Bendiocarb, Benfuracarb, Benthiavalicarb-isopropyl, Butocarboxim, Carbaryl, Carbofuran, Carbosulfan, Cycloate, Dazomet, Di-allate, Diethofencarb, Dimepiperate, Esprocarb, Ethiofencarb, Fenobucarb (BPMC), Fenothiocarb, Fenoxycarb, Furathiocarb, Iprovalicarb, Isoprocarb, Methiocarb, Methomyl, Metolcarb, Molinate, Oxamyl, Pebulate, Phenmedipham, Pirimicarb, Promecarb, Propamocarb, Propham, Propoxur, Pyributicarb, Thiobencarb, Thiodicarb, Tri-allate, Trimethacarb, Vernolate, XMC

Imidazolinone (5)

Fenamidone, Imazamox, Imazapic, Imazaquin, Imazethapyr

Neonicotinoid (7)

Acetamiprid, Clothianidin, Dinotefuran, Imidacloprid, Nitenpyram, Thiacloprid, Thiamethoxam

Organophosphate (64)

Acephate, Anilofos, Azamethiphos, Azinphos-ethyl, Azinphos-methyl, Bensulide, Cadusafos, Carbophenothion, Chlorfenvinphos, Chlorpyrifos, Chlorpyrifos-methyl, Demeton-S-methyl, Diazinon, Dichlorvos, Dicrotophos, Dimethoate, Dimethylvinphos, Edifenphos, EPN, Ethion, Ethoprophos, Etrimfos, Fenamiphos, Fenthion, Fonofos, Fosthiazate, Imicyafos, Iprobenfos, Isazofos, Isoxathion, Malathion, Mecarbam, Methamidophos, Methidathion, Mevinphos, Monocrotophos, Omethoate, Oxydemeton-methyl, Parathion, Phenthoate, Phorate, Phosalone, Phosmet, Phosphamidon, Phoxim, Piperophos, Pirimiphos-ethyl, Pirimiphos-methyl, Profenofos, Prothiofos, Pyraclofos, Pyrazophos, Pyridaphenthion, Quinalphos, Sulprofos, Tebupirimfos, Terbufos, Tetrachlorvinphos, Thiometon, Tolclofos-methyl, Triazophos, Tribufos, Trichlorfon, Vamidothion

Pyrethroid (14)

Bifenthrin, Cycloprothrin, Cyhalothrin-lambda, Cypermethrin, Deltamethrin, Etofenprox, Fenpropathrin, Fenvalerate, Flucythrinate, Fluvalinate, Halfenprox, Permethrin, Phenothrin, Tralomethrin

Strobilurin (8)

Azoxystrobin, Fluacrypyrim, Kresoxim-methyl, Metominostrobin, Orysastrobin, Picoxystrobin, Pyraclostrobin, Trifloxystrobin

Triazine (12)

Ametryn, Atrazine, Cyanazine, Dimethametryn, Hexazinone, Metribuzin, Prometryn, Propazine, Simazine, Simetryn, Terbuthylazine, Terbutryn

Triazole (26)

Amisulbrom, Azaconazole, Bitertanol, Cafenstrole, Carfentrazone-ethyl, Cyproconazole, Difenoconazole, Diniconazole, Epoxiconazole, Fenbuconazole, Fluquinconazole, Flusilazole, Hexaconazole, Imibenconazole, Metconazole, Myclobutanil, Paclobutrazol, Penconazole, Propiconazole, Simeconazole, Tebuconazole, Tetraconazole, Triadimefon, Triadimenol, Triticonazole, Uniconazole

31



Compound name

Urea (34)

Azimsulfuron, Bensulfuron-methyl, Chlorfluazuron, Chlorimuron-ethyl, Chlorotoluron, Chlorsulfuron, Cyclosulfamuron, Daimuron, Diafenthiuron, Diflubenzuron, Diuron, Ethametsulfuron-methyl, Ethoxysulfuron, Flucetosulfuron, Flufenoxuron, Forchlorfenuron, Halosulfuron-methyl, Hexaflumuron, Imazosulfuron, Isoproturon, Linuron, Lufenuron, Metazosulfuron, Methabenzthiazuron, Metobromuron, Nicosulfuron, Novaluron, Pencycuron, Rimsulfuron, Teflubenzuron, Thidiazuron, Thifensulfuron-methyl, Tribenuron-methyl, Triflumuron

Others/ Unclassified

(146)

Acibenzolar-S-methyl, Alachlor, Allidochlor, Ametoctradin, Amitraz, Benfuresate, Bentazone, Benzobicyclon, Benzoximate, Bifenazate, Bifenox, Boscalid, Bromacil, Bromobutide, Bromoxynil, Bupirimate, Buprofezin, Butachlor, Butafenacil, Carbendazim, Carboxin, Carpropamid, Cartap, Chinomethionat, Chlorantraniliprole, Chloridazon, Chromafenozide, Cinmethylin, Clethodim, Clofentezine, Clomazone, Cyazofamid, Cyflufenamid, Cymoxanil, Cyprodinil, Cyromazine, Diflufenican, Dimethachlor, Dimethenamid, Dimethomorph, Diphenamid, Diphenylamine, Dithianon, Dithiopyr, Ethaboxam (EBX), Ethoxyquin, Etoxazole, Famoxadone, Fenarimol, Fenazaquin, Fenhexamid, Fenoxanil, Fenpyroximate, Fentrazamide, Ferimzone, Fipronil, Flonicamid, Fluazinam, Flubendiamide, Fludioxonil, Flufenacet, Flumiclorac-pentyl, Flumioxazin, Fluopicolide, Fluopyram, Flusulfamide, Flutolanil, Fluxapyroxad, Hexythiazox, Imazalil, Inabenfide, Indanofan, Indoxacarb, Iprodione, Isoprothiolane, Isopyrazam, Lactofen, Mandipropamid, Mefenacet, Mefenpyr-diethyl, Mepanipyrim, Mepronil, Metalaxyl, Methoxyfenozide, Metolachlor, Metrafenone, Napropamide, Nitrapyrin, Nuarimol, Ofurace, Oxadiazon, Oxadixyl, Oxaziclomefone, Pendimethalin, Penoxsulam, Penthiopyrad, Picolinafen, Pretilachlor, Probenazole, Prochloraz, Propachlor, Propanil, Propisochlor, Propyzamide, Pymetrozine, Pyrazolynate, Pyrazoxyfen, Pyribenzoxim, Pyridaben, Pyridalyl, Pyridate, Pyrifenox, Pyrifluquinazon, Pyrimethanil, Pyrimidifen, Pyriminobac-methyl E, Pyriminobac-methyl Z, Pyrimisulfan, Pyriproxyfen, Pyroquilon, Quinmerac, Quinoclamine, Saflufenacil, Sethoxydim, Spirodiclofen, Spirotetramat, Sulfoxaflor, TCMTB, Tebufenozide, Tebufenpyrad, TFNA [4-trifluoromethyl nicotinic acid], TFNG [N-(4-trifluoromethylnicotinoyl)glycine], Thenylchlor, Thiabendazole, Thiazopyr, Thifluzamide, Thiocyclam, Thiophanate-methyl, Tiadinil, Tolfenpyrad, Tolylfluanid, Triclopyr, Tricyclazole, Triflumizole, Trifluralin, Zoxamide

32

Fig. 3. TIC obtained by LC-MS/MS analysis of (a) matrix-matched standard

in human serum with 379 pesticides at 100 ng/mL (4 μL injection) and (b)

TIC of control (non-fortified) serum sample

34

Fig. 4. TIC obtained by LC-MS/MS analysis of (a) matrix-matched standard

in human urine with 380 pesticides at 100 ng/mL (4 μL injection) and (b) TIC

of control (non-fortified) urine sample

36

Table 4. The number of pesticides with recoveries between 70-120% with RSDs below 20% in the recovery test from different

extraction methods for 379 Pesticides in 100 μL of human serum (fortification Level at 250 ng/mL, n = 3)

Type of method

Preparation method scaled-down from

Extraction solvent Extraction reagent No. of analytes

% of analytes

A Original method

Acetonitrile (400 μL)

MgSO4 (40 mg) NaCl (10 mg)

344 90.8

B AOAC 2007.01

1% HOAc in acetonitrile (400 μL)

MgSO4 (40 mg) NaOAc (10 mg)

341 90.0

C EN 15662



Na3Citrate·2H2O (10 mg) Na2HCitr·1.5H2O (5mg)

341 90.0

37

Table 5. The number of pesticides with recoveries between 70-120% with RSDs below 20% in the recovery test from different

extraction methods for 379 Pesticides in 100 μL of human urine (fortification Level at 250 ng/mL, n = 3)

Type of method

Preparation method scaled-down from

Extraction solvent Extraction reagent No. of analytes

% of analytes

A Original method



360 94.7

B AOAC 2007.01

1% HOAc in acetonitrile (400 μL)

MgSO4 (40 mg) NaOAc (10 mg)

359 94.5

C EN 15662



Na3Citrate·2H2O (10 mg) Na2HCitr·1.5H2O (5mg)

357 93.9

38

Method validation

With the final established analytical method, several validation tests were

conducted, and the result of each parameter was verified with adequate criteria.

The five validation parameters to be determined were limit of quantitation

(LOQ), linearity of calibration, accuracy and precision, and recovery and

matrix effect.

Limit of quantitation (LOQ). The LOQ was defined as the lowest concentration

or mass of the analyte that was validated with acceptable accuracy (European

Commission, 2017). One way to determine the LOQ is to verify that the S/N

on the chromatogram is greater than 10 (De Bièvre et al., 2005). In this study,

the LOQs ranged from 10 to 250 ng/mL for the pesticide multiresidues in serum

and urine were verified.

In the serum sample, 358 compounds of the total 379 pesticides (94.5%

of the total) had LOQs of 10 ng/mL (Fig. 5). It is remarkable that a sufficiently

low LOQ level was obtained with reliable selectivity while analyzing 379

pesticides simultaneously. Most of the pesticides were detectable at very low

concentration, even though almost 400 pesticides were analyzed

simultaneously. The ratio of pesticides satisfying LOQ 10 ng/mL was higher

than Dulaurent et al. (2010)’s screening analysis of more than 300 pesticides in

blood using MS2 and MS3 mode of LC-IT-MS (Dulaurent et al., 2010). Among

the remaining 21 (5.5%) components with LOQs higher than 10 ng/mL, the

LOQ levels of 11 (2.9%), 7 (1.8%), and 3 (0.8%) analytes were determined at

25, 50, and 100 ng/mL, respectively. These LOQs are sufficiently low enough

to detect cases of acute pesticide poisoning because pesticide concentrations in

39

blood have been reported to be from a few tens of ng/mL to several tens of

μg/mL in most acute poisoning cases (Miyazaki et al., 1989; Lee et al., 1999;

Lacassie et al., 2001b; Hikiji et al., 2013). These results demonstrate that this

analytical method can sufficiently determine pesticides from an unknown

sample without further concentration of the sample.

In the urine sample, the large majority of pesticides (364 compounds,

95.8% of the total 380 pesticides) were found to have an LOQ at 10 ng/mL, the

minimum level in the analytical methods (Fig. 6). Among the remaining 16

(4.3%) pesticides, nine (benfuresate, bifenox, fenvalerate, inabenfide,

milbemectin A3, parathion, terbufos, trifluralin, and a flonicamid metabolite

TFNG [N-(4-trifluoromethylnicotinoyl)glycine]) showed LOQ at 25 ng/mL.

Six pesticides (diphenylamine, dithianon, flonicamid, nitrapyrin, thiocyclam,

and a flonicamid metabolite TFNA [4-trifluoromethyl nicotinic acid]) showed

LOQ at 50 ng/mL, and only butocarboxim had an LOQ at 100 ng/mL. No

pesticide showed a higher (150 and 250 ng/mL) LOQ. Those compounds with

LOQ > 10 ng/mL also had sufficiently low detectability for forensic

applications because urinary concentrations of parent compounds have been

reported to range from sub to hundreds of ng/mL in cases of acute pesticide

intoxication or in some biomonitoring investigations (Hattori et al., 1982;

Montesano et al., 2007; Cazorla-Reyes et al., 2011; Usui et al., 2012; Quansah

et al., 2016). Therefore, with the established analytical method, 380 pesticides

can be determined in a urinary sample without further concentration of the

sample extract.

40

Fig. 5. Pie chart showing distribution of LOQs (ng/mL) for 379 pesticides in

serum for the final optimized analytical method. Light gray bar, 10 ng/mL;

gray bar, 25 ng/mL; dark gray bar, 50 ng/mL; black bar, 100 ng/mL

41

358(94.5%)

11 (2.9%)

7(1.8%) 3 (0.8%)

10 25 50 100

(ng/mL)

42

Fig. 6. Pie chart showing distribution of LOQs (ng/mL) for 380 pesticides in

urine for the final optimized analytical method. Light gray bar, 10 ng/mL;

gray bar, 25 ng/mL; dark gray bar, 50 ng/mL; black bar, 100 ng/mL

43

364(95.8%)

9 (2.4%) 6 (1.6%)

1 (0.3%)

10 25 50 100(ng/mL)

44

Linearity of calibration. Calibration was defined as the determination of the

relationship between the observed signal from the target analyte in the sample

extract and known quantities of the analyte prepared as standard solutions

(European Commission, 2017). The degree of dependence established between

the two variables can be expressed by the correlation coefficient (r2). The closer

the r2 value is to 1, the better is the fit between signals and their concentrations

(quantitative information).

Before the correlation coefficients of target analytes were determined,

linear ranges were investigated. For the serum sample, there were various linear

ranges (Table 6) because each analyte had a different LOQ and upper limit of

quantitation (concentration of the highest calibration standard). Most of the

compounds (92.4%) had a linear range from 10 to 250 ng/mL, which was the

largest range in the analytical method. However, 19 compounds (5.0%) showed

linear ranges from their LOQ to 250 ng/mL (9 for 25-250 ng/mL, 7 for 50-250

ng/mL, and 3 for 100-250 ng/mL). For the remaining 10 (2.6%) components, a

linear range could not be drawn to 250 ng/mL due to the saturation effect of the

signal at higher concentrations, resulting in linear ranges from their LOQ to 150

ng/mL for 8 analytes (6 for 10-150 ng/mL and 2 for 25-150 ng/mL) and 2 for

10-100 ng/mL.

For the correlation coefficient (r2) of the 379 target compounds in serum

(Table 7), 356 pesticides (93.9%) had r2 greater than 0.990, indicating that most

of the pesticides had a quantitative property with good linearity within these

linear ranges. Correlation coefficients for 17 compounds (4.5%) were within

0.980-0.990, and four pesticides were within 0.900-0.980. It was expected that

these ranges of correlation coefficients were also acceptable for the

45

multiresidue screening method. Diafenthiuron and tolyfluanid had somewhat

poor correlation coefficients (0.835 and 0.879, respectively).

For the urine sample, Most of the pesticides (95.3%) had a linear range

over 10-250 ng/mL concentrations, the largest linear range in this analytical

method (Table 8). Linear ranges of 16 compounds (4.3%) were from LOQ to

250 ng/mL (nine for 25-250 ng/mL, six for 50-250 ng/mL, and one for 100-250

ng/mL). Dazomet and carbosulfan had linear ranges of 10-150 ng/mL and 10-

100 ng/mL, respectively, due to signal saturation at higher concentration. The

linear ranges for those two compounds were reduced by excluding higher

concentrations to maintain quantitative properties at lower level concentrations.

Of the 380 target pesticides in urine, correlation coefficients of 366

(96.3%) were r2 ≥0.990 (Table 9), meaning that most of the compounds had

excellent quantitative properties with good linearity within their linear ranges.

Correlation coefficients of 10 (2.6%) compounds were within 0.980-0.990,

adequate to maintain quantitative properties for screening purposes. The

remaining 4 (1.1%) compounds (benfuracarb, diphenylamine, flumioxazin, and

trifluralin) showed somewhat poor linearities (r2 within 0.9771-0.9790).

46

Table 6. Distribution of linear ranges for 379 pesticides in serum for the final established analytical method

Linear range (ng/mL)

No. of analytes

% of analytes

Remarks

10-250 350 92.4 -

25-250 9 2.4 Abamectin B1a, Aldicarb, Butocarboxim, Imazamox, Iprodione, MCPA, Milbemectin A3, Propham, Terbufos

50-250 7 1.8 Diphenylamine, Fenvalerate, TFNA, Thiometon, Tolylfluanid, Tralomethrin, Trifluralin

100-250 3 0.8 Inabenfide, Nitrapyrin, Thiocyclam

10-150 6 1.6 Asulam, Benfuracarb, Bentazone, Cafenstrole, Fluxapyroxad, Mecoprop-P

25-150 2 0.5 Bifenox, Diafenthiuron

10-100 2 0.5 Carbosulfan, Dazomet

Sum 379 100 -

47

Table 7. Distribution of correlation coefficients (r2) for 379 pesticides in serum for the final established analytical method

r2 No. of analytes

% of analytes

Remarks

≥0.990 356 93.9 -

0.980-0.990 17 4.5 Abamectin B1a, Aldicarb, Benfuracarb, Bentazone, Bromacil, Cyazofamid, Cypermethrin,

Demeton-S-methyl, Flufenacet, Fluopicolide, Iprodione, Methomyl, Metribuzin, Pyrimisulfan, Sulfoxaflor, Thiamethoxam,

Thiocyclam

0.900-0.980 4 1.1 Cyhalofop-butyl, Cyhalothrin-lambda, Diphenylamine, Parathion

<0.900 2 0.5 Diafenthiuron, Tolylfluanid

Sum 379 100 -

48

Table 8. Distribution of linear ranges for 380 pesticides in urine for the final established analytical method

Linear range (ng/mL)

No. of pesticides

(%)

% of analytes

Remarks

10-250 362 95.3 -

25-250 9 2.4 Benfuresate, Bifenox, Fenvalerate, Inabenfide, Milbemectin A3, Parathion, Terbufos, TFNG, Trifluralin

50-250 6 1.6 Diphenylaimine, Dithianon, Flonicamid, Nitrapyrin, TFNA, Thiocyclam

100-250 1 0.3 Butocarboxim

10-150 1 0.3 Dazomet

10-100 1 0.3 Carbosulfan

Sum 380 100 -

49

Table 9. Distribution of correlation coefficients (r2) for 380 pesticides in urine for the final established analytical method

r2 No. of analytes

% of analytes

Remarks

≥0.990 366 96.3 -

0.980-0.990 10 2.6 Abamectin B1a, Benfuresate, Bifenox, Butocarboxim, Cafenstrole, Cyromazine, Dazomet, Fluxapyroxad, Nitrapyrin, Thiometon

<0.980 4 1.1 Benfuracarb, Diphenylamine, Flumioxazin, Trifluralin

Sum 380 100 -

50

Accuracy and precision. Accuracy was defined as the degree of closeness of

the determined value to the nominal or known true value, and precision as the

closeness of agreement among a series of measurements obtained from multiple

sampling of the same homogenous sample (US FDA, 2013).The accuracy value

was calculated as the average of the measured values (%) for the replicates (n

= 5) at each level, and its precision was expressed as RSD (%):

RSD, % =Standard deviation

Average× 100

For the serum sample, the representative results at a QC level of 150 ng/mL

were given using scatter plots of intra- and inter-day tests to verify accuracy

and precision values of 379 pesticides at a glance (Fig. 7). In both of intra- and

inter-day, most of the pesticides were located in a square zone of accuracy; 80-

120% and RSD; 0-20% showing excellent accuracies and precisions. Only a

few pesticide such as aldicarb, bifenox, diafenthiuron, imazamox, thiocyclam

in intra-day and diafenthiuron, imazamox, inabenfide, lepimectin A3,

nitrapyrin, parathion, thiocyclam, trifluralin in inter-day were out of the zone.

There was no relationship between there compounds and those of chemical

group or tR.

To summarize and evaluate accuracy and precision results including all

QC levels, data were statistically processed based on reasonable criteria.

According to the criteria of US FDA (2013), accuracy values are 80-120%

(RSD ≤20%) at the LOQ level and 85-115% (RSD ≤15%) at higher levels (US

FDA, 2013). In this study, there were four LOQs (10, 25, 50, and 100 ng/mL)

for each analyte depending on their sensitivity and the LOQ for most of the

51

pesticides (94.5%) was 10 ng/mL. Therefore, the former criterion of accuracy

and precision was applied for the 10 ng/mL QC level, and the latter was applied

for the other (50, 150, and 250 ng/mL) QC levels to reduce the complexity of

reorganizing data.

52

Fig. 7. Scatter plots for 379 pesticides in serum to show accuracies and

precisions (RSD) in (a) intra-day and (b) inter-day tests (at 150 ng/mL of QC

level)

54

As shown in Fig. 8, the percentages of pesticides satisfying the criteria

at 10 ng/mL were 87.3 and 86.5% in the intra- and inter-day tests, respectively.

At the 50, 150, and 250 ng/mL levels, the percentages meeting the criteria were

91.3, 97.6, and 96.0% in the intra-day test and 90.8, 96.0, and 94.7% in the

inter-day test, respectively. Most of the pesticides satisfied the accuracy and

precision (RSD) criteria, and the proportions of pesticides meeting the criteria

in intra-day were slightly higher than those of inter-day. In the case of

tolyfluanid, its poor linearity did not affect its quantitation property, showing

an excellent accuracy (85.1-106.0%) with an acceptable precision (6.0-14.7%)

in intra- and inter-day tests. Four pesticides (bifenox, diafenthiuron, imazamox,

and nitrapyrin) did not satisfy the criteria at all QC levels in the intra- and inter-

day tests. In the case of bifenox, nitrapyrin, and imazamox, the accuracy values

were 68.8-129.8% (RSD; 10.1-33.6%) within those of linear ranges. This

indicated that those pesticides had acceptable accuracy and precision ranges in

the screening analysis. Diafenthiuron had poor accuracy (116.5-184.1%) and

precision results (RSD; 40.8-62.3%) in both tests, due to the poor linearity of

the calibration (r2 = 0.835) and instrumental reproducibility. Therefore,

diafenthiuron should be determined by qualitative confirmation rather than

quantitative confirmation when analyzing an unknown serum sample. The

accuracy and precision of diafenthiuron has been reported as excellent with

buffered QuEChERS approaches in tomatoes, evaluated with recovery test, by

using LC-MS/MS45. Because there is no literature on the analysis of

diafenthiuron in serum or blood to our knowledge, further research to improve

accuracy and precision of diafenthiuron in serum is needed.

55

The results confirmed that the true concentration value for most

pesticides in serum can be determined excellently and reliably, and is also valid

over several days. Therefore, using the established preparation method and

instrumental condition, biomonitoring for pesticide multiresidues can be

conducted simultaneously in agricultural or other cases of pesticide intoxication.

56

Fig. 8. Percentage of 379 pesticides satisfying the accuracy values within 80-

120% (RSD ≤20%) at 10 ng/mL and within 85-115% (RSD ≤15%) at 50,

150, and 250 ng/mL in the intra-day (grey bars) and inter-day (dark grey bars)

tests using the final established method in serum sample

57

87.3 86.5 91.3 90.8 97.6 96.0 96.0 94.7

0.0

20.0

40.0

60.0

80.0

100.0

Intra-day Inter-day Intra-day Inter-day Intra-day Inter-day Intra-day Inter-day

10 ng/mL 50 ng/mL 150 ng/mL 250 ng/mL

Freq

uenc

y, %

Intra-day Inter-day

58

For the urine sample, the representative results at a QC level of 150

ng/mL were given using scatter plots of intra- and inter-day tests to verify

accuracy and precision values of 380 pesticides at a glance, (Fig. 9). In both

plots, most of the pesticides were within the square zone of accuracy; 80-120%

and RSD; 0-20%, showing the excellent accuracy and precision values at a QC

level of 150 ng/mL. Only a few compounds in the intra-day (diafenthiuron,

dithianon, nitrapyrin, TFNG, and trifluralin) and inter-day (dithianon, TFNG,

and trifluralin) were out of the zone, still within the square zone of accuracy;

60-140% and RSD; 0-30%. Individual accuracy results of inter-day were closer

to 100% than those of intra-day.

The accuracy and precision data were statistically processed to

summarize and evaluate the results including all QC levels based on reasonable

reference criteria. For the QC level of 10 ng/mL, 89.7% and 87.1% of 380

analytes satisfied the accuracy criteria in the intra- and inter-day measurements,

respectively (Fig. 10). For QC levels of 50, 150, and 250 ng/mL, 92.1-97.6%

of analytes in intra-day and 90.8-97.4% in inter-day fell within the acceptable

criteria. The ratio satisfying the accuracy criteria was highest at 150 ng/mL in

both intra- and inter-day testing, and the number of pesticides meeting the

criteria under the intra-day condition was slightly higher than that under inter-

day conditions at all QC levels. Although nearly 400 pesticides were extracted

together from a QC sample and analyzed simultaneously in only 15 minutes,

most of the compounds did not lose their chemical properties or react with each

other, and LC-MS/MS showed excellent throughput abilities to select, detect,

and quantify hundreds of pesticides with high reliability. Furthermore, this

bioanalytical method was verified as valid by inter-day results.

59

Only a few compounds showed poor accuracy and precision at all QC

levels in urine. One compound (TFNG) in the intra-day testing and two

compounds (benfuresate and nitrapyrin) in the inter-day testing did not satisfy

acceptable accuracy or precision criteria at all QC levels. Four compounds

(butocarboxim, dithianon, parathion, and trifluralin) did not satisfy the same

criteria in both the intra- and inter-day test. Those seven compounds were not

detectable at 10 ng/mL, the lowest concentration level in this analytical method,

and so had somewhat poor sensitivity compared with other pesticides. Accuracy

ranges for these compounds at all QC levels were within 62.1-145.9%,

sufficiently valid to identify and quantify for rapid screening purposes.

From these results, biomonitoring for multiresidual pesticides using this

analytical method can be performed with high reliability in forensic and clinical

applications.

60

Fig. 9. Scatter plots for 380 pesticides in urine to show accuracies and

precisions (RSD) in (a) intra-day and (b) inter-day tests (at 150 ng/mL of QC

level)

62


with RSD ≤20% at a QC level of 10 ng/mL and the accuracy range of 85-115

with RSD ≤ 15% at 50, 150, and 250 ng/mL levels under intra-day (grey bars)

and inter-day (dark grey bars) conditions in urine sample

63

89.7 87.192.1 90.8

97.6 97.4 95.8 95.3

0.0

20.0

40.0

60.0

80.0

100.0

Intra-day Inter-day Intra-day Inter-day Intra-day Inter-day Intra-day Inter-day

10 ng/mL 50 ng/mL 150 ng/mL 250 ng/mL

Freq

uenc

y, %

Intra-day Inter-day

64

Recovery. Recovery was defined as the proportion of the analyte remaining at

the point of final determination, following its addition immediately prior to

extraction (European Commission, 2017). The extraction efficiency of the

preparation step is excellent when the recovery rate of a compound is close to

100%. A greater recovery rate could increase the sensitivity of target analytes.

Recovery can also be a parameter of trueness (accuracy) for the analytical

method. Recovery and its variation (RSD) have been regarded as accuracy and

precision parameters in many bioanalytical methods (Cazorla-Reyes et al., 2011;

Kim et al., 2014). Generally, a recovery rate of 70-120% (RSD ≤20%) is an

acceptable trueness range (European Commission, 2017). These criteria have

already been utilized for the verification of extraction efficiency for

multiresidual pesticides in which the original QuEChERS extraction solvent

(acetonitrile) and unbuffered salts (MgSO4 and NaCl) were superior to other

buffered reagents, as described in the above section.

For the serum sample, the recovery data at a fortification level of 50

ng/mL were presented according to representative chemical groups (Fig. 11),

for the verification of recovery rates of 379 pesticides at a glance. All of the

compounds belonging to the neonicotinoid, strobilurin, triazine, triazole groups

and most of the pesticides classified as the avermectin/spinsyn, carbamate,

organophosphate, pyrethroid, urea, and others/unclassified groups showed

excellent recovery range (70-120%). However, half of the pesticides belonging

to the aryloxyalkanoic/aryloxyphenoxypropionic acid and most of the

imidazolinone pesticides showed lower recovery ranges (<70%). Most of the

pesticides in the two groups are acidic compounds or zwitterions, so the

extraction efficiencies for these polar compounds were decreased.

65

To summarize and evaluate recovery data at all treated levels, the results

were classified in accordance with Mol et al. (2008) and Jia et al. (2014) by five

different recovery ranges (<30%, 30-50%, 50-70%, 70-120%, and >120%) for

multiresidue pesticides (Mol et al., 2008; Jia et al., 2014). RSD results were

also classified with two groups (0-20% and >20%). As shown in Table 10, 85.8,

90.2, and 91.8% of target analytes satisfied the recovery range of 70-120% with

RSD ≤20% at fortification levels of 10, 50, and 250 ng/mL, respectively. The

percentages of pesticides with a recovery rate of less than 70% were 3.4-6.6%

at all fortification levels. Only 1.8% and 1.4% of pesticides had a recovery rate

greater than 120% at 10 and 250 ng/mL of the treated level and no pesticide

included at 50 ng/mL. The overall recovery results were similar with those in

Kim et al. (2014) using mini-QuEChERS (AOAC 2007.1 buffer salts) for whole

blood analysis, in which approximately 83% and 11% of 215 pesticides had a

recovery range of 80-100% and 100-150% respectively (Kim et al., 2014).

However, among the pesticides with a recovery rate under 60%, acephate,

aldicarb, dimepiperate, diphenylamine, EPN, fluazinam, methamidophos,

omethoate, pyridalyl, teflubenzuron, and triclopyr showed a better recovery rate

(72.3-114.2%) in our study, most likely because cleanup was omitted after

extraction in the sample preparation procedure.

66

Fig. 11. Distribution to show recovery values for 379 pesticides classified

into the representative chemical groups (treated at 50 ng/mL in serum)

68


fortification levels of 10, 50, and 250 ng/mL in serum for the final established

analytical method

Recovery Range

RSD Treated Level No. of analytes (%)

10 ng/mL 50 ng/mL 250 ng/mL

<30% 0-20% 1 (0.3) 3 (0.8) 0 (0.0)

>20% 0 (0.0) 1 (0.3) 0 (0.0)

30-50% 0-20% 1 (0.3) 3 (0.8) 6 (1.6)

>20% 1 (0.3) 3 (0.8) 0 (0.0)

50-70% 0-20% 7 (1.8) 13 (3.4) 10 (2.6)

>20% 3 (0.8) 2 (0.5) 0 (0.0)

70-120% 0-20% 325 (85.8) 342 (90.2) 348 (91.8)

>20% 13 (3.4) 8 (2.1) 0 (0.0)

>120% 0-20% 5 (1.3) 0 (0.0) 4 (1.1)

>20% 2 (0.5) 0 (0.0) 1 (0.3)

N.D.1) 21 (5.5) 4 (1.1) 10 (2.6)

Sum 379 (100) 379 (100) 379 (100) 1)Not determined due to out of linear range.

69

From the results, 18 compounds were verified as out of the recovery

range of 70-120% with RSD ≤20% at all fortification levels (Table 11). Two

compounds (inabenfide and nitrapyrin) had high recovery rates (123.4 and

173.9%, respectively). The reason for the recovery rates exceeding 120%

despite the matrix-matching was that low sensitivity of inabenfide and

nitrapyrin (LOQ; 100 ng/mL) and insufficient calibration points caused

quantitation error. In contrast, 16 compounds showed a low recovery range of

15.4-66.2% or poor recovery RSD (20.5-52.7%). Among the pesticides with

low recovery rates, nine compounds (2,4-D, imazamox, imazapic, imazaquin,

imazethapyr, MCPA, mecoprop-P, quinmerac, and the flonicamid metabolite

TFNA [4-trifluoromethyl nicotinic acid]) were acidic compounds or zwitterion

(Turner, 2015; Chemicalize.org, 2017). Therefore, it is thought that those

analytes were present as ionic forms in the serum at almost neutral pH, and

some of them remained in the water layer at the partitioning step and were not

recovered. It has been reported that the recovery rate for some of these pH-

dependent compounds increased by extracting with an organic acid or an acidic

buffer in many matrices including blood (Pareja et al., 2011; Carneiro et al.,

2013; Kim et al., 2014). Our study verified that these pesticides obtained a

greater recovery rate when using method (B) (AOAC 2007.1) or (C) (EN 15662)

in the optimizing sample preparation step (Fig. 12). Although the pesticides

listed in Table 11 were out of the recovery criteria range, the accuracy and

precision were excellent except for a few analytes, such that screening of the

pesticides is not a problem.

70

Table 11. Pesticides for which recovery test results were not within 70-120% (RSD ≤20%) at all treated levels (10, 50, and 250

ng/mL), and intra-day accuracy results with RSD (serum)

No. Compound

name

Treated level 10 ng/mL 50 ng/mL 250 ng/mL

Remarks Chemical

group Recovery, %

(RSD, %) Accuracy, %

(RSD, %) Recovery, %




(RSD, %) 1 2,4-D Aryloxy-

alkanoic acid 58.7

(10.0) 107.0 (12.7)

52.2 (14.8)

93.9 (12.6)

60.7 (8.9)

104.1 (12.5)

pKa 2.73 (20-25 ℃) (Turner, 2015)

2 Abamectin B1a

Avermectin N.D.1) N.D.1) 38.2 (58.8)

92.2 (35.6)

60.0 (11.8)

98.1 (10.7)

-

3 Bifenox Nitrophenyl Ether

N.D.1) N.D.1) 58.9 (22.7)

83.8 (20.1)

N.D.1) N.D.1) -

4 Diafenthiuron Urea N.D.1) N.D.1) 57.5 (26.0)

116.5 (62.3)

N.D.1) N.D.1) -

5 Etofenprox Pyrethroid 52.3 (5.9)

96.8 (2.7)

55.8 (3.6)

88.2 (6.3)

59.5 (7.2)

85.2 (13.3)

-

6 Imazamox Imidazolinone N.D.1) N.D.1) 15.4 (47.8)

68.8 (32.7)

34.9 (9.5)

72.6 (19.9)

Zwitterion (Turner, 2015)

7 Imazapic Imidazolinone 31.4 (11.4)

105.0 (13.2)

21.6 (7.1)

85.3 (9.7)

31.8 (9.4)

80.9 (10.5)


8 Imazaquin Imidazolinone 52.8 (27.0)

93.9 (14.0)

46.3 (6.3)

92.1 (7.9)

52.6 (0.7)

89.3 (6.0)

pKa 3.8 (20-25 ℃) (Turner, 2015)

9 Imazethapyr Imidazolinone 56.6 (6.1)

97.0 (13.9)

41.7 (4.2)

92.0 (4.5)

54.1 (3.0)

88.1 (12.0)


10 Inabenfide Unclassified N.D.1) N.D.1) N.D.1) N.D.1) 123.4 (16.5)

102.8 (8.0)

-

11 MCPA Aryloxy- alkanoic acid

N.D.1) N.D.1) 48.3 (20.5)

99.4 (9.8)

66.2 (10.9)

90.1 (4.4)

pKa 3.73 (20-25 ℃) (Turner, 2015)

12 Mecoprop-P Aryloxy- alkanoic acid

60.0 (7.0)

97.0 (11.3)

48.7 (30.0)

74.8 (16.4)

N.D.1) N.D.1) pKa 3.78 (20-25 ℃) (Turner, 2015)

13 Nitrapyrin Unclassified N.D.1) N.D.1) N.D.1) N.D.1) 173.9 (44.2)

110.3 (14.7)

-

71


No. Compound

name

Treated level 10 ng/mL 50 ng/mL 250 ng/mL

Remarks Chemical

group Recovery, %






(RSD, %) 14 Quinmerac Quinoline-

carboxylic acid

25.1 (6.6)

88.9 (6.7)

29.3 (6.3)

98.8 (10.2)

35.3 (3.2)

85.1 (8.0)

pKa 4.32 (20-25 ℃) (Turner, 2015)

15 TFNA Nicotinic acid N.D.1) N.D.1) 25.5 (15.9)

100.0 (14.5)

32.6 (2.3)

87.6 (14.3)

pKa 2.622), 3.992) (calculated)

(Chemicalize.org, 2017) 16 Thiocyclam Nereistoxin

analogue N.D.a N.D.a N.D.a N.D.a 49.0

(13.0) 85.8

(12.1) -

17 Tolylfluanid Phenyl-sulfamide

N.D.a N.D.a N.D.a 104.6 (14.7)

31.0 (15.4)

85.3 (8.8)

-

18 Tralomethrin Pyrethroid N.D.a N.D.a 72.0 (52.7)

131.2 (12.4)

63.2 (10.7)

99.6 (12.9)

-

1)Not determined due to out of linear range.

2)Calculated values using Chemicalize.org by ChemAxon.

72

Fig. 12. Recovery results (treated at 250 ng/mL in serum) of three different

QuEChERS extraction methods for pH-dependent pesticides that showed

lower recovery rate in the validation test

73

27.2

5.7 6.815.6 14.6

24.4 24.2

0.36.9

42.0 41.4 45.4

64.1 63.0

50.9

66.9

18.627.4

71.8 74.679.8 80.9

103.0

73.464.4

69.4

42.5

0.0

20.0

40.0

60.0

80.0

100.0

120.0

Rec

over

y ra

te, %

Type A(Original method)

Type B(AOAC 2007.01)

Type C(EN 15662)

74

In conclusion, most of the pesticides were well recovered at all treated

levels in this study. The recovery rate of pH-dependent compounds could be

increased by adjusting the sample pH.

For the urine sample, a distribution chart of recovery was introduced in

accordance with representative chemical groups (Fig. 13). All the pesticides

belonging to carbamate, imidazolinone, neonicotinoid, strobilurin, triazine, and

triazole groups were within the recovery range of 70-120% at a treated level of

50 ng/mL, showing excellent recovery. It was remarkable that, although most

imidazolinone and aryloxyalkanoic/aryloxyphenoxypropionic acid compounds

are acidic or zwitterions, their extraction efficiencies were not reduced under

unbuffered conditions. The pyrethroid group showed the lowest percent

recovery (71.4%) of the 70-120% recovery group.

The distribution chart (Table 12) to summarize recovery results showed

that 328 (86.3%), 335 (88.2%), and 338 (88.9%) of the pesticides were within

the recovery range of 70-120% (RSD ≤20%) at 10, 50, and 250 ng/mL,

respectively. One to 21 (0.3 to 5.5%) pesticides were included in the same range

with RSD >20%. Some pesticides (3.3-10.4%) belonged to the lower recovery

rate group (30-70%), but no pesticide showed recovery rates less than 30%.

Only 2.1% and 0.3% of pesticides showed a recovery rate greater than 120% at

the 10 and 50 ng/mL levels, respectively, and no pesticide was included in this

range at 250 ng/mL.

The recovery results were compared to the report of Cazorla-Reyes et

al. (2011) in which 204 pesticides in urine samples were extracted and purified

at once by SPE (C18 cartridge) and then analyzed using GC-IT-MS/MS (117

pesticides) and LC-MS/MS (87 pesticides) (Cazorla-Reyes et al., 2011). The

75

number of pesticides satisfying the recovery rate of 70-120% with RSD ≤20%

in the Cazorla-Reyes et al. report is similar to our results (Cazorla-Reyes et al.,

2011). However, some compounds (e.g., acephate, bendiocarb, flufenoxuron,

and simazine) that fell outside of the criterion of 50 ng/mL in their report

showed excellent recovery rates (86.9-98.6% with RSD 5.9-13.1%) at the same

treated level in our study, and, vice versa, a few pesticides (e.g., bifenthrin,

hexythiazox, lufenuron, parathion, permethrin, and tebufenpyrad) showed

better recovery range (71-111% with RSD 2-8%) in the report (Cazorla-Reyes

et al., 2011).

From the recovery data, most of the pesticides showed high extraction

efficiency by this bioanalytical method. In spite of the diverse chemical

properties of the different pesticides, strong extraction/partitioning reagents

used in the preparation step maintained overall excellent recovery rates.

Additionally, further cleanup steps were excluded to prevent the loss of target

analytes.

76

Fig. 13. Distribution of recovery rates for 380 pesticides by representative

chemical groups at fortification levels of 50 ng/mL in urine

78


fortification levels of 10, 50, and 250 ng/mL in urine for the final established

analytical method

Recovery range

RSD Treated level No. of pesticides (%)

10 ng/mL 50 ng/mL 250 ng/mL

<30% 0-20% 0 (0.0) 0 (0.0) 0 (0.0)

>20% 0 (0.0) 0 (0.0) 0 (0.0)

30-50% 0-20% 3 (0.8) 2 (0.5) 6 (1.6)

>20% 1 (0.3) 6 (1.6) 1 (0.3)

50-70% 0-20% 4 (1.1) 13 (3.4) 31 (8.2)

>20% 4 (1.1) 1 (0.3) 1 (0.3)

70-120% 0-20% 328 (86.3) 335 (88.2) 338 (88.9)

>20% 16 (4.2) 21 (5.5) 1 (0.3)

>120% 0-20% 6 (1.6) 0 (0.0) 0 (0.0)

>20% 2 (0.5) 1 (0.3) 0 (0.0)

N.D.1) 16 (4.2) 1 (0.3) 2 (0.5)

Sum 380 (100) 380 (100) 380 (100)

1)Not determined due to out of linear range.

79

Matrix effect. The matrix effect was defined as the influence of one or more

co-extracted compounds from the sample on the measurement of the analyte’s

concentration or mass (European Commission, 2017). The matrix effect when

analyzing pesticides using LC with mass spectrometer is a common

phenomenon (Hajšlová and Zrostlı́ková, 2003). Kebarle and Tang (1993) first

reported the mechanism of matrix effect in ESI mode (Kebarle and Tang, 1993).

One technique for minimizing the matrix effect is sample dilution (Hernández

et al., 2005; Ferrer et al., 2011a; Panuwet et al., 2016). In this study, therefore,

0.1 mL of the serum and urine sample was extracted with four times larger

extraction solvent volume (0.4 mL of acetonitrile). The extract solution was

also partitioned into an organic solvent layer and water layer using MgSO4 and

NaCl to remove polar compounds from the organic solvent layer that may affect

the matrix effect. According to equation described above, the matrix effect of

each compound can be expressed as percentage enhancement (> 0%) or

suppression (< 0%). The farther away the percentage is from zero (0%), the

larger is the matrix effect.

For the serum sample, the average matrix effect for the 379 pesticides

was -6.8%, which means that the response on LC-MS/MS for most compounds

was somewhat suppressed by the matrix. The matrix effect data and tR of each

pesticide were plotted on a scatter plot (Fig. 14) for the verification of a

relationship between the two variables. The scatter graph showed that matrix

effects of most pesticides were within the soft effect zone during the time of

first pesticide elution to 9 minutes. From 9 minutes to the time of the last

pesticide elution, however, more than 50% of the compounds were out of soft

effect zone, showing large instrumental signal suppression.

80

Fig. 14. Scatter plot to show tR and matrix effect of 379 pesticides in serum

81

-100-80-60-40-20

020406080

100

0 3 6 9 12

Mat

rix e

ffect

(%)

Retention time, tR (min)

Medium(20% to 50%)

Soft(-20% to 20%)

Medium(-20% to -50%)

Strong(<-50%)

Strong(>50%)

82

According to Kmellár et al. (2008) and Ferrer et al. (2011), the results

were divided into six ranges and three groups (Fig. 15), corresponding to a soft

effect when the value was within -20% to 0% or 0% to 20%, a medium effect

within -50% to -20% or 20% to 50%, and a strong effect below -50% or above

50% (Kmellár et al., 2008; Ferrer et al., 2011a; Ferrer et al., 2011b). The number

of pesticides with a soft effect (between -20% and 20%) was 349 (92.1%),

which was considered as no matrix effect (Ferrer et al., 2011a). Therefore, those

compounds in this group were not prone to be affected by serum, indicating that

solvent-based calibration could be possible for quantitation. The compounds

within the medium and strong effect groups were 25 (6.6%) and 5 (1.3%),

respectively. For those analytes susceptible to influences of the serum matrix,

it is necessary to make quantitation data using matrix-matched calibration to

avoid enhancement or suppression of responses.

For the urine sample, the average matrix effect of the 380 target

pesticides in urine was -4.1%. This negative percentage indicates that the urine

matrix tended to slightly suppress the signal intensity of target compounds in

LC-MS/MS. For verification of matrix effect for each pesticide and correlation

with retention time, the scatter plot of matrix effect and tR were shown as Fig.

16. From the initiation time of pesticide elution to around 5 minutes, a large

number of pesticides were located in below -20% of matrix effect. The matrix

effects were weakened after approximately 5 minutes to end of the elution time.

This results indicated that most of the polar urinary matrices were co-eluted

with target pesticides in the early stages of analytical time (~5 min), so affected

considerable signal suppression of target compounds. \

83

The summary of matrix effects for the 380 pesticides classified into

three groups and 6 ranges (Fig. 17) showed that most of the pesticides (74.2%)

were included in the soft effect group, in which 179 (47.1%) compounds fell

between -20% and 0%, and 103 (27.1%) pesticides fell between 0% and 20%.

Within the soft group, matrix effects are considered negligible on LC-MS/MS

(Ferrer et al., 2011b). Therefore, it is possible to determine the concentration of

real urine samples using solvent-only (matrix-free) standard solution rather

than matrix-matched solution. The numbers of compounds in the medium and

strong groups were 66 (17.3%) and 32 (8.4%), respectively. These groups were

susceptible to interfering influences in urine, thus requiring matrix-matched

calibration for correct quantitation.

In conclusion, using sample dilution in the preparation step, most of the

pesticides showed a very small matrix effect, regarded as no effect by the

human serum and urine matrix during quantitation. Only a few compounds with

a large matrix effect need alternative approaches such as matrix-matching or

standard addition method in the quantitative process in the biological samples.

84

Fig. 15. Distribution of matrix effects (%) for 379 pesticides classified into

soft effect (light grey bars, -20% to 0% and 0% to 20%), middle effect (grey

bars, -50% to -20% and 20% to 50%), and strong effect (dark grey bars, <-

50% and >50%) in human serum samples

85

3 (0.8%) 18 (4.7%)

313 (82.6%)

36 (9.5%)7 (1.8%) 2 (0.5%)

0

50

100

150

200

250

300

350

<-50%Strong

-50%-(-20)%Medium

-20%-0%Soft

0%-20%Soft

20%-50%Medium

>50%Strong

Num

ber o

f the

com

poun

ds

Matrix effect

86

Fig. 16. Scatter plot between retention time (tR) and matrix effect for 380

target pesticides in urine

87

-100-80-60-40-20

020406080

100120140160

0 3 6 9 12

Mat

rix e

ffect

(%)

Retention time, tR (min)

Medium(20% to 50%)

Soft(-20% to 20%)

Medium(-20% to -50%)

Strong(<-50%)

Strong(>50%)

88

Fig. 17. Summary of matrix effects for 380 pesticides classified into soft

effect (light grey bars, -20% to 0% and 0% to 20%), middle effect (grey bars,

-50% to -20% and 20% to 50%), and strong effect (dark grey bars, <-50%

and >50%) in human urine samples

89

18 (4.7%)

48 (12.6%)

179 (47.1%)

103 (27.1%)

18 (4.7%) 14 (3.7%)

020406080

100120140160180200

<-50%Strong

-50%-(-20)%Medium

-20%-0%Soft

0%-20%Soft

20%-50%Medium

>50%Strong

Num

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Matrix effect

90

Conclusions

A quantitative screening method for rapid and simultaneous analysis of 379

pesticides in serum and 380 pesticides in urine was developed using LC-

MS/MS. High speed positive/negative switching electrospray ionization (ESI)

mode was utilized, and scheduled multiple reaction monitoring (MRM) was

employed. The limit of quantitation was 10 ng/mL for more than 94% of target

compounds in both matrices, showing sufficiently low to detect multiresidues

at trace levels. The scaled-down QuEChERS procedure was optimized and used

for sample preparation after three versions of QuEChERS were compared for

recovery. The established method was fully validated for important analytical

parameters such as linearity of calibration, accuracy and precision, recovery,

and matrix effect. The correlation coefficients (r2) of calibration were ≥0.990

for 93.9% (serum) and 96.3% (urine) of target compounds. In the accuracy and

precision tests, most of the pesticides showed excellent results in intra- and

inter-day conditions. In the recovery tests at 10, 50, and 250 ng/mL, 85.8-91.8%

of all target compounds in serum and 86.3-88.9% in urine satisfied the recovery

range of 70-120% (RSD ≤20%). The average matrix effect for all target

compounds in serum and urine were -6.8% and -4.1%, respectively. The

established analytical methods in this study can be applied to the identification

of pesticide intoxication cases and biomonitoring in total diet study, food

toxicology, agricultural, forensic and clinical sciences.

91

Part 2



Using GC-MS/MS

92



Reference standards (analytical grade) or stock solutions (1,000 mg/L) of each

pesticide were purchased from Sigma-Aldrich (St. Louis, MO, USA), Dr.

Ehrenstorfer (Augsburg, Germany), and Ultra Scientific (North Kingstown, RI,

USA). Acetonitrile and acetone (HPLC grade) were obtained from Fisher

Scientific (Seoul, South Korea). Magnesium sulfate anhydrous (MgSO4, purity

≥99.5%) were purchased from Sigma-Aldrich. Sodium chloride (NaCl, 99.0%)

was obtained from Samchun (Gyeonggi-do, South Korea). Ceramic

homogenizers (2 mm) were provided from Ultra Scientific.

Individual reference standards were subjected to dissolving with a

solvent such as acetonitrile or acetone to give each stock solution of 1,000 mg/L.

These solutions and commercial stock solutions were mixed so that the

concentration of the mixed standard solution was 10 mg/L. This solution was

further diluted to make the working solutions using in MS/MS profiling,

preparing calibration curves, and validation studies. All the prepared solutions

were stored at -20 °C until the study was conducted.

GC-MS/MS instrumental conditions

GC-MS/MS analysis was carried out on a Shimadzu GCMS-TQ8040 triple

quadrupole mass spectrometer coupled to a GC-2010 plus equipped with an

AOC-20i autosampler (Kyoto, Japan). For the mass spectrometer, electron

energy of the EI was 70 eV and temperature values of ion source and interface

were 230 and 250 °C, respectively. Detector voltage was maintained at 1.4 kV

93

during the entire instrumental performance. Argon (≥99.999%) was used as

collision inductive dissociation (CID) gas. For the gas chromatograph, a 3.5-

mm Topas GC glass liner with wool (Restek, Bellefonte, PA, USA) was inserted

in the inlet. The inlet temperature was 280 °C and the pulsed injection at a

pressure of 250 kPa was used. Injection mode was splitless and the injection

volume was 2 μL. A capillary column was Rxi-5Sil MS (30 m × 0.25 mm i.d.,

0.25 μm df, Restek, Bellefonte, PA, USA). The oven temperature program (30

min in total) was initialized at 90 °C (held for 3 min), ramped to 120 °C at

20 °C/min, and then to 300 °C at 8 °C/min (held for 3 min). Helium (≥99.999%)

was used as carrier gas and total column flow was 1.50 mL/min (constant). For

the multiresidue MRM data processing, GCMS solution version 4.30 was

utilized.

Establishment of scheduled MRM

Each standard solution of 1 to 10 mg/L was injected (2 μL) to obtain a full scan

spectrum in the range of mass to charge ratio (m/z) 50-500. One or two of

precursor ion(s) were selected in the spectrum and then a product scan with

various collision energies (CE; 3-42 V) was conducted. Among the product ions

fragmented, two of them with optimum CE were finally selected as a quantifier

and a qualifier ion based on those of selectivity from other compounds, signal

intensity, and peak shape on the chromatogram. The loop time of MRM mode

was 0.30 sec and the minimum MRM window was set to ±0.30 min from

retention time (tR) so that the dwell time was at least 15 ms.

94

Sample preparation using modified QuEChERS

Pesticides in human serum and urine sample were extracted by the previously

established procedures in Part 1. In brief, 100 μL of an aliquot was extracted

with 400 μL of acetonitrile in 2-mL of microcentrifuge tube (Eppendorf,

Hamburg, Germany). The extract was shaken with two ceramic homogenizer

beads for 1 min at 1,200 rpm using a Geno Grinder (1600 MiniG SPEX Sample

Prep, Metuchen, NJ, USA). MgSO4 (40 mg) and NaCl (10 mg) were added to

the tube for solvent-water layer partitioning. This step was exothermic by

MgSO4, so the extract was subjected to cooling on an ice bath. The tube was

centrifuged for 5 min at 13,000 rpm (16,800 g) using a microcentrifuge (17TR,

Hanil Science, Seoul, Korea), and then without dSPE cleanup, 200 μL of

organic supernatant was matrix-matched with 50 μL acetonitrile. The aliquot

was equivalent to 0.2 mL per one mL of the final extract. Two μL of the final

extract was injected into GC-MS/MS for the target compound analysis.

Validation of methodology

For the determination of LOQ, different concentrations at 10, 25, 50, 100, 150,

and 250 ng/mL of matrix-matched procedure standards of serum and urine were

injected into GC-MS/MS, respectively. The result of each MRM chromatogram

was investigated whether satisfying the signal to noise ratio (S/N) greater than

10 at the LOQ level. If a compound did not meet the S/N criterion, higher

concentration satisfying S/N criteria was selected as LOQ. The linearity of

calibration was determined (n = 5) by the correlation coefficient (r2) of the

calibration curve within a range from 10 to 250 ng/mL. To correct quantitation

properties at low concentrations, a weighting factor of 1/x was adopted.

95

Accuracy and precision tests were performed using four different levels of

serum or urine quality control (QC) samples (10, 50, 150, and 250 ng/mL). The

tests were evaluated under intra-day and inter-day conditions. The intra-day

condition was that 5 replicates of each QC level were subjected to analysis in a

day. The inter-day condition was that one QC sample of each level was analyzed

per day during five successive days. The recovery test was conducted at treated

levels of 10, 50, and 250 ng/mL (n = 3). Blank samples of serum or urine was

fortified with pesticides, respectively, and treated as the same preparation

procedure as described above. In the GC-MS/MS analysis, some target

pesticides were affected by the matrix effect severely, so the recovery rate for

each pesticide was corrected by using matrix-matched standard calibration. The

matrix effect of each target analyte was also calculated by comparing a slope of

the calibration curve of the matrix-matched standards and that of the calibration

curve of the solvent-only standards using the following equation:



Safety information




implemented.

96


Characteristics of pesticide to be studied

A total of 58 pesticides was selected as research compounds at first (Table 13).

Among the pesticides, 41 compounds are generally undetectable or have very

low sensitivity on LC/MS system. The other 17 pesticides (binapacryl,

bromophos, chlorpropham, cyanophos, cyfluthrin, dichlofluanid, dicofol,

disulfoton, endosulfan-sulfate, ethalfluralin, isofenphos, isofenphos-methyl,

nitrothal-isopropyl, oxyfluorfen, parathion-methyl, silafluofen, and

spiromesifen) are known to be amenable on LC/MS (EU Reference

Laboratories for Residues of Pesticides), however, could not ionized on LC-

MS/MS in Part 1. For the chemical group of pesticides, Most of the pesticides

were organochlorine (40 compounds). Major pesticide groups such as

organophosphate (7), pyrethroid (3), carbamate (1) were also included, and the

remaining of 7 pesticides was included in minor groups or unclassified. Among

the 56 pesticides, 8 compounds were metabolites of organochlorines such as

DDT (o,p'-DDD, p,p'-DDD, o,p'-DDE, and p,p'-DDE), endosulfan (endosulfan-

sulfate), heptachlor (heptachlor-epoxide), quintozene (pentachloroaniline and

pentachlorothioanisole). These metabolites were included as target analytes

because they have already been detected in human biological samples (Zhou et

al., 2011; Genuis et al., 2016) or have been identified as major metabolites in

experiments with apes (Müller et al., 1978).

97

Table 13. List of pesticides to be studied and their chemical groups

No. Compound name Chemical group Remarks 1 Aldrin Organochlorine - 2 BHC-alpha Organochlorine - 3 BHC-beta Organochlorine - 4 BHC-delta Organochlorine - 5 BHC-gamma Organochlorine - 6 Binapacryl1) Others/Unclassified - 7 Bromophos Organophosphate - 8 Bromopropylate Organochlorine - 9 Chlordane-cis Organochlorine -

10 Chlordane-trans Organochlorine - 11 Chlorfenapyr Organochlorine - 12 Chlorobenzilate Organochlorine - 13 Chlorothalonil Organochlorine - 14 Chlorpropham Carbamate - 15 Chlorthal-dimethyl Others/Unclassified - 16 Cyanophos Organophosphate - 17 Cyfluthrin Pyrethroid Isomer

mixture (4 peaks)

18 DDD-o,p' Organochlorine DDT metabolite

19 DDD-p,p' Organochlorine DDT metabolite

20 DDE-o,p' Organochlorine DDT metabolite

21 DDE-p,p' Organochlorine DDT metabolite

22 DDT-o,p' Organochlorine - 23 DDT-p,p' Organochlorine - 24 Dichlobenil Organochlorine - 25 Dichlofluanid Organochlorine - 26 Dicloran Organochlorine - 27 Dicofol Organochlorine - 28 Dieldrin Organochlorine - 29 Disulfoton Organophosphate - 30 Endosulfan-alpha Organochlorine - 31 Endosulfan-beta Organochlorine -

98


No. Compound name Chemical group Remarks 32 Endosulfan-sulfate Organochlorine Endosulfan

metabolite 33 Endrin Organochlorine - 34 Ethalfluralin Others/Unclassified - 35 Etridiazole Organochlorine - 36 Fenclorim Others/Unclassified - 37 Fenitrothion Organophosphate - 38 Fthalide Organochlorine - 39 Heptachlor Organochlorine - 40 Heptachlor-epoxide Organochlorine Heptachlor

metabolite 41 Isofenphos Organophosphate - 42 Isofenphos-methyl Organophosphate - 43 Methoxychlor Organochlorine - 44 Nitrothal-isopropyl Others/Unclassified - 45 Oxyfluorfen Others/Unclassified - 46 Parathion-methyl Organophosphate - 47 Pentachloroaniline Organochlorine Quintozene

metabolite 48 Pentachlorothioanisole Organochlorine Quintozene

metabolite 49 Procymidone Organochlorine - 50 Quintozene Organochlorine - 51 Silafluofen Pyrethroid - 52 Spiromesifen Others/Unclassified - 53 Tefluthrin Pyrethroid - 54 Tetradifon Organochlorine - 55 Vinclozolin Organochlorine - - Captafol2) Organochlorine - - Captan2) Organochlorine - - Folpet2) Organochlorine -

1)Compound excluded from the final analytical method validation study in

serum.

2)These compounds were excluded from the list of the final validation in serum

and urine

99

Optimization of MRM

MRM optimization on GC-MS/MS was conducted in the order of 1) mass full

scan with m/z 50-500, 2) precursor ion selection, 3) product ion scan under CID,

and 4) determination of product ion. At first, all of the pesticides to be studied

were subjected to full scan analysis and successfully obtained their specific

spectrum patterns. According to each pesticide spectrum data, most of the target

analytes were fragmented in the EI source and existed abundantly as various

fragment ions in mass analyzers. Therefore, the precursor ions were selected

from one or two of the fragment ions as well as the molecular ion. Each selected

precursor ion was subjected to further fragmentation with various CE voltages,

and then its product ions were determined depending on intensity or selectivity.

Because the resolution of the triple quadrupole mass spectrometer was unit

mass, two transition profiles of each pesticide were finally established

according to the guideline of SANTE/11813/2017 (European Commission,

2017). Each MRM transition was selected respectively as a quantifier ion for

quantitation processing and a qualifier ion for verification of the false positive

(Table 14).

100

Table 14. The optimized GC-MS/MS parameters including retention times (tR),

MRM transitions for each pesticide

No. Pesticide Name tR (min)

Transition Precursor ion > Product ion (CE, V)

Qualifier Qualifier 1 Aldrin 16.35 263 > 193 (30) 263 > 191 (30) 2 BHC-alpha 12.78 181 > 145 (15) 219 > 183 (9) 3 BHC-beta 13.40 181 > 145 (18) 219 > 183 (9) 4 BHC-delta 14.32 181 > 145 (15) 219 > 183 (9) 5 BHC-gamma 13.64 181 > 145 (15) 219 > 183 (12) 6 Binapacryl 19.08 83 > 55 (9) 83 > 53 (15) 7 Bromophos 16.81 331 > 93 (30) 329 > 93 (27) 8 Bromopropylate 21.71 341 > 183 (21) 183 > 76 (27) 9 Chlordane-cis 18.17 377 > 268 (27) 377 > 266 (24) 10 Chlordane-trans 17.86 377 > 268 (27) 377 > 266 (24) 11 Chlorfenapyr 19.08 247 > 227 (15) 247 > 200 (27) 12 Chlorobenzilate 19.47 251 > 111 (27) 139 > 75 (27) 13 Chlorothalonil 14.04 264 > 168 (27) 266 > 168 (24) 14 Chlorpropham 12.20 127 > 65 (21) 213 > 171 (9) 15 Chlorthal-dimethyl 16.40 301 > 223 (24) 332 > 301 (18) 16 Cyanophos 13.75 243 > 109 (15) 125 > 79 (9)

17-1 Cyfluthrin_1 24.53 163 > 127 (6) 226 > 206 (15) 17-2 Cyfluthrin_2 24.65 163 > 127 (6) 226 > 206 (15) 17-3 Cyfluthrin_3 24.72 163 > 127 (6) 226 > 206 (15) 17-4 Cyfluthrin_4 24.78 163 > 127 (6) 226 > 206 (15) 18 DDD-o,p' 18.85 235 > 165 (24) 165 > 163 (30) 19 DDD-p,p' 19.74 235 > 165 (24) 235 > 199 (18) 20 DDE-o,p' 17.93 246 > 176 (30) 318 > 246 (27) 21 DDE-p,p' 18.70 246 > 176 (27) 318 > 246 (21) 22 DDT-o,p' 19.67 235 > 165 (24) 235 > 199 (18) 23 DDT-p,p' 20.55 235 > 165 (24) 235 > 199 (18) 24 Dichlobenil 7.73 171 > 100 (27) 136 > 100 (9) 25 Dichlofluanid 16.05 224 > 123 (15) 224 > 77 (30) 26 Dicloran 13.10 206 > 176 (12) 206 > 124 (27) 27 Dicofol 16.63 139 > 111 (15) 250 > 139 (15) 28 Dieldrin 18.81 279 > 207 (27) 263 > 193 (30)

101


No. Pesticide Name tR (min)

Transition Precursor ion > Product ion (CE, V)

Qualifier Qualifier 29 Disulfoton 14.20 142 > 109 (6) 186 > 153 (6) 30 Endosulfan-alpha 18.17 241 > 206 (15) 339 > 160 (21) 31 Endosulfan-beta 19.54 339 > 160 (18) 339 > 267 (9) 32 Endosulfan-sulfate 20.43 272 > 237 (18) 272 > 235 (15) 33 Endrin 19.30 263 >191 (30) 263 >193 (28) 34 Ethalfluralin 12.10 276 > 202 (15) 316 > 276 (12) 35 Etridiazole 9.29 211 > 183 (12) 183 > 140 (15) 36 Fenclorim 12.81 189 > 104 (15) 224 > 189 (15) 37 Fenitrothion 15.88 277 > 260 (6) 260 > 125 (15) 38 Fthalide 16.67 243 > 215 (18) 272 > 243 (18) 39 Heptachlor 15.47 272 > 237 (18) 272 > 235 (18) 40 Heptachlor-epoxide 17.29 353 > 263 (18) 353 > 282 (15) 41 Isofenphos 17.30 213 > 121 (15) 185 > 121 (15) 42 Isofenphos-methyl 16.95 199 > 121 (12) 199 > 93 (27) 43 Methoxychlor 21.82 227 > 169 (27) 227 > 212 (18) 44 Nitrothal-isopropyl 16.68 236 > 194 (12) 194 > 148 (12) 45 Oxyfluorfen 18.84 252 > 146 (30) 252 > 170 (30) 46 Parathion-methyl 15.25 263 > 109 (15) 263 > 246 (6) 47 Pentachloroaniline 14.75 263 > 192 (21) 265 > 194 (21) 48 Pentachlorothioanisole 15.99 296 > 263 (18) 263 > 193 (30) 49 Procymidone 17.55 283 > 96 (12) 285 > 96 (12) 50 Quintozene 13.52 295 > 237 (18) 237 > 143 (24) 51 Silafluofen 25.50 286 > 258 (12) 286 > 207 (15) 52 Spiromesifen 21.28 272 > 254 (9) 272 > 185 (24) 53 Tefluthrin 14.35 177 > 127 (18) 197 > 141 (15) 54 Tetradifon 22.29 356 > 159 (15) 356 > 229 (9) 55 Vinclozolin 15.22 212 > 172 (15) 285 > 212 (15) - Captafol1) 21.03 183>79 (15) 313>79 (21) - Captan1) 17.63 149 > 79 (18) 149 > 105 (6) - Folpet1) 17.77 260 > 130 (15) 260 >232 (9)

1)These compounds were excluded from the list of the final validation in serum

and urine.

102

Determination of final selected pesticides to be validated

After MRM optimization and determination of tR, recovery samples and matrix-

matched standard solutions of serum and urine at 250 ng/mL were injected

respectively on GC-MS/MS to select final pesticides for validation tests. As a

result, 54 of the total 58 pesticides were found to be free of serious degradation

or false positives. Captafol, captan, and folpet which are phthalimide (PI)

organochlorines, however, were not recovered at all in both of recovery samples

(Fig. 18). Because these PI organochlorines are very unstable, so hydrolyzed in

aqueous conditions or in broad ranges of pH (Turner, 2015). Furthermore, it has

been reported that captan and folpet were rapidly degraded into

tetrahydrophthalimide (THPI) and PI, respectively, in the blood (half-lives;

0.97 s for captan and 4.9 s for folpet) (Gordon et al., 2001). Therefore, captan,

captafol, and folpet were excluded from the list of pesticide to be developed.

Instead of determination of captan, captafol, and folpet, THPI and PI could be

biomarkers of these pesticides in serum and urine (Berthet et al., 2011).

In another case, binapacryl, a dinitrophenol pesticide, was found to be

interfered severely by serum matrix (Fig. 19). The MRM transition of this

compound was comprised of very small m/z (quantifier; 83 > 55 and qualifier;

83 > 53, see Table 14), so it could be easily overlaid by small molecule or

fragment with similar tR. On the other hand, there was no interference or overlay

for binapacryl in urine. Therefore, binapacryl was excluded from the final list

of the pesticide only in serum.

In summary, among the 58 pesticides, 54 analytes in serum and 55

analytes in urine were selected as final validation compounds.

103

Validation of analytical method

Limit of quantitation (LOQ) and linearity of calibration. There are various

criteria for the determination of LOQ (Kruve et al., 2015). Among them, S/N

approach has been widely used for development of bioanalytical methodologies

(Pauwels et al., 1999; Pozzebon et al., 2003). LOQs were determined for the

minimum concentration of S/N ≥10 in serum or urine sample. For the 55 target

pesticides, 53 satisfied LOQ criterion at a concentration of 10 ng/mL in both

samples (Fig. 20). Endosulfan-beta did not satisfy this criterion in both samples,

thus higher concentration (25 ng/mL, S/N ≥10) was selected as LOQ. The LOQ

of binapacryl was also determined at 25 ng/mL in urine sample, but could not

be determined in serum sample due to overlaps with interferences (see Fig. 19).

In many application reports including acute and intoxication, local monitoring,

and exposure from agricultural field, some pesticides and their metabolites

studied in this study have been detected higher than 10 ng/mL in blood or urine

(Beltran et al., 2001; Columé et al., 2001; Lacassie et al., 2001b; López et al.,

2001; Sharma et al., 2015). Therefore, this bioanalytical method had sufficient

sensitivity for the determination of pesticides in agricultural and forensic fields.

The linearity of calibration for each pesticide was verified by preparing

a calibration curve ranging from LOQ to 250 ng/mL. The correlation coefficient

(r2) of calibration tells how strong a relationship between the two variables

(concentration and signal) is. The closer the r2 value is to 1, the stronger the

positive relationship. The r2 of target compounds were greater than 0.9935 in

serum and 0.9925 in urine (Fig. 20). It indicates that relationship between

concentration and signal of all target pesticides was highly strong, thus ensuring

quantitative properties.

104

Fig. 18. Structures for phthalimide organochlorines, (a) captafol, (b) captan,

and (c) folpet. MRM chromatograms for matrix-matched standards of (d)

captafol, (e) captan, (f) folpet, and (g)-(i) these recovery samples in serum,

and MRM chromatograms for matrix-matched standards of (j) captafol, (k)

captan, (l) folpet, and (m)-(o) these recovery samples in urine

106

Fig. 19. MRM chromatograms of (a) solvent-only standard, (b) matrix-

matched standard in serum, and (c) matrix-matched standard in urine for

binapacryl.

108

Fig. 20. Individual LOQs and correlation coefficients (r2) of 55 pesticides for

the final established analytical method in serum and urine

109

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110

Accuracy and precision. The accuracy and precision tests at 10, 50, 150, and

250 ng/mL were conducted under intra-day and inter-day conditions through

the QC sample (a sample with a known quantity of analyte (US FDA, 2013))

analysis. According to US FDA, the acceptable criteria of accuracy ranges with

precision ranges (expressed as RSD) were 80-120% with RSD ≤20% at an LOQ

and 85-115% with RSD ≤15% at higher concentration (US FDA, 2013). In this

study, the LOQ criteria were applied at the QC level of 10 ng/mL and the other

criteria at 50, 150, and 250 ng/mL. The accuracy ranges of the target pesticides

in serum were 83.5-119.3% with RSD 1.1-19.8% in the intra-day condition and

81.4-118.4% with RSD 0.6-14.4% in the inter-day condition. The accuracy

ranges in urine were 91.5-114.2% in the intra-day with RSD 0.5-19.1% in the

intra-day and 91.8%-120.4% with RSD 0.9-16.8%. For the serum QC samples,

the numbers of 54 target pesticides satisfying the criteria were 53 (98.1% of the

total) at the QC level of 10 ng/mL, and 52 to 54 (96.3% to 100%) at 50, 150,

and 250 ng/mL, respectively (Fig. 21, a). Except for LOQ issues, p,p’-DDT and

methoxychlor (accuracy 108.7% and 114.8%, respectively) at 50 ng/mL were

slightly out of the RSD criteria (19.8% and 19.1%, respectively) in intra-day.

For the urine QC samples, the numbers of 55 target pesticides satisfying the

criteria were 52 to 53 (94.5% to 96.4% of the total) at the QC level of 10 ng/mL,

and 55 (100%) at 50, 150, and 250 ng/mL, respectively (Fig. 21, b). Except for

LOQ issues, chlorothalonil at 10 ng/mL were slightly out of the accuracy

criteria (120.4% with RSD 14.7%) in the inter-day. From the results, the most

of the pesticides obtained excellent and robust bioanalytical methods in serum

and urine using GC-MS/MS. A few pesticides slightly out of the criteria are

also available for screening purpose. Therefore, this analytical methods can be

111

performed with high reliability in forensic investigation, clinical biomonitoring,

or for occupational/non-occupational exposure.

Recovery. The recovery not only indicates the extraction efficiency of a

pesticide in the sample treatment but also it can be another accuracy parameter.

The European Commission recommended an acceptable recovery range from

70 to 120% with RSD ≤20% (European Commission, 2017). The recovery tests

were performed at fortification levels of 10, 50, and 250 ng/mL in serum and

urine sample. As a result, recovery ranges were 70.4-118.2% (RSD; 0.3-14.8%)

in serum and 70.5-119.2% (RSD; 0.2-17.5%) in urine at all treated levels. These

results indicate that all target pesticides fell the acceptable recovery criteria

within their detection ranges. The scaled-down QuEChERS procedures

established for LC amenable pesticides also exhibited strong and rugged

extraction efficiencies for relatively non-polar GC amenable pesticides.

112


with RSD ≤20% at a QC level of 10 ng/mL and the accuracy range of 85-115

with RSD ≤ 15% at QC levels of 50, 150, and 250 ng/mL in (a) serum and

(b) urine under intra-day (grey bars) and inter-day (dark grey bars) conditions

114

Matrix effect. The matrix effect is a common effect on GC and mass

spectrometer system. This phenomenon in biological samples can be reduced

by sample dilution (López et al., 2001) and be corrected by internal standard or

preparing calibration curve with matrix-matched standards (Saito et al., 2012;

Luzardo et al., 2015). In this study, 100 μL of serum or urine samples were

extracted with four times larger volumes of acetonitrile (400 μL) and matrix-

matched standards were used to overcome matrix effect.

The matrix effect was expressed as percentage by the equation as

described above. If a percentage is 0%, not matrix effect is observed. A value

farther from 0% tells that matrices contribute to more enhancement or

suppression of the detector response. The average matrix effects for target

pesticides in serum and urine were 8.8% and 1.7%, respectively. Serum

matrices had a greater effect than urine matrices. Matrix effect values were

divided into 6 ranges and three groups (soft, medium, and strong matrix effect,

see Fig 22). Most of the matrix effects for the target pesticides (46 compounds

for serum and 38 for urine) were included in soft effect range (between -20 and

20% of the percentages). Within the range, matrices are considered to not affect

detector responses of target analytes, thus negligible (He et al., 2015). The other

groups of pesticides (8 for serum and 17 for urine) including medium (-50% to

-20% or 20% to 50%) and strong (<-50% or >50%) showed larger matrix effect.

These pesticides within the ranges need matrix-matched standard calibration to

correct the quantitation.

115

Conclusions

A rapid and simultaneous bioanalytical method for 54 pesticides in human

serum and for 55 pesticides in human urine was established and validated using

GC-MS/MS. A pulsed injection at a high pressure (250 kPa) on the GC injector

and an EI mode on the ion source of MS/MS were used and a scheduled MRM

of each target compound were established for an effective and high-throughput

analysis. For the sample preparation, a modified QuEChERS procedure without

dSPE cleanup was adopted for application in small volumes (100 μL) of aliquot.

Except for binapacryl in serum, the false positive was not found on the MRM

window of each pesticide in both of matrix. The target LOQ of the established

methodology was at 10 ng/mL and 53 of all the pesticides met the criteria in

both of serum and urine, showing sufficiently low to detect multiresidues at

trace levels in both of serum and urine samples. The correlation coefficients (r2)

were ≥0.990 for all target analytes within the linear range from LOQ to 250

ng/mL. For ruggedness of the method, the accuracy and precision were

conducted under the intra- and inter-day conditions and most of the compounds

showed excellent validation results. The recovery rates were 70.4-119.2% with

the RSD of 0.2-17.5% at fortification levels of LOQ, 50, and 250 ng/mL,

showing a high extraction efficiency in this preparation procedure. The

averages of the matrix effect (%) in serum and urine samples were 8.8% and

1.7%, respectively. This determination method of screening multiresidual

pesticides can be useful in forensic or medical case of acute pesticide

intoxication where highly fast monitoring is essential.

116

Fig. 22. Distribution of matrix effects for 380 pesticides in (a) serum and (b)

urine. The matrix effect was classified into soft effect (light grey bars, -20%

to 0% and 0% to 20%), middle effect (grey bars, -50% to -20% and 20% to

50%), and strong effect (dark grey bars, <-50% and >50%)

119

Chapter II

Analysis of Neonicotinoids (Clothianidin,

Imidacloprid, and Thiamethoxam) and Pesticide

Multiresidues in Honey Bee, Pollen, and Honey

Using LC-MS/MS and GC-MS/MS

120

Introduction

Benefits from honey bee

Honey bee is an important pollinator and considerably contribute to the

ecosystem and agriculture in the earth. Pollination is essential to reproductive

system of wild flowers, and bees mediate pollination by their foraging behavior

(Corbet et al., 1991). It was reported that almost all of pollination (90-100%)

for many agricultural crops such as apple, almond, onion, and carrot is carried

out by honey bee (Johnson, 2010). The value of pollination by insect is

estimated at more than $200 billion, accounting for 9.5% of the total value of

global agricultural production (vanEngelsdorp and Meixner, 2010). Fruit

productivity by honey bee pollination is superior to that by artificial pollination.

In the Republic of Korea, the rate of apple fruit set was 40.9% for honey bee

(Apis mellifera) whereas 26.7% for artificial (hand) method (Lee et al., 2008).

Honey bee also provide various apicultural products such as honey, pollen, and

wax.

Honey bee Colony Collapse Disorder (CCD)

Recently, inexplicable and massive honey bee disappearances were observed.

This phenomenon began to be a serious issue from fall 2006, as the beekeeping

industry in the USA experienced catastrophic losses (Johnson et al., 2009). A

similar disaster was also reported in Europe (Benjamin et al., 2012). This

syndrome was named Colony Collapse Disorder (CCD) and its definition is that

(1) the apparent rapid loss of adult bee workers resulting in weak or dead

colonies with excess brood populations compared to adult bee populations; (2)

121

the noticeable lack of dead bee workers both within and surrounding the hive;

and (3) the delayed invasion of hive pests (e.g., small hive beetles and wax

moths) and kleptoparasitism from neighboring honey bee colonies (Cox-Foster

et al., 2007; vanEngelsdorp et al., 2009). CCD caused a 50-90% loss of the

beekeeping colonies in the United States (Cox-Foster et al., 2007).

There are various possible causes such as larger animal predatory

damage, mite (Acarapis woodi and Varroa destructor), microorganism, virus,

global warming, urbanization, abuse of pesticide, genetically modified (GM)

crop, and electromagnetic radiation (Stankus, 2008; Sainudeen Sahib, 2011).

Neonicotinoid, a suspicious chemical leading to CCD

There are some opinions that pesticide poisoning, especially caused by

neonicotinoids is related to CCD. Neonicotinoid is a relatively modern

pesticide introduced in the early 1990s. Its safety than other pesticides made it

popular and neonicotinoid became one of the most commonly used insecticide

globally. In 2008, the agrochemical market share of neonicotinoids was 24%

(€1.5 billion) of total volume (€6.3 billion) and neonicotinoid had gained an 80%

(€0.77 billion) share of a total insecticidal seed treatment market (€0.96 million)

(Jeschke et al., 2011). Recently, however, various honey bee malfunctions

caused by neonicotinoids have been reported, such as impaired winterization,

decreased immunity, promotion of a viral pathogen replication which may lead

to CCD (Di Prisco et al., 2013; Lu et al., 2014).

Among the neonicotinoids, clothianidin, imidacloprid, and

thiamethoxam are highly neurotoxic to bees (European Commission, 2005;

European Commission, 2006; European Food Safety Authority, 2008). The

122

European Union (EU) restricted the temporary use of these controversial

neonicotinoids in crops attractive to pollinators since 2013 (European

Commission, 2013). After the prohibition, the European Commission again

asked the European Food Safety Authority (EFSA) for an updated risk

assessment of the neonicotinoids, and the EFSA confirmed the risks of these

three pesticides to honey bees as well as wild bees on February, 2018 (European

Food Safety Authority, 2018a; European Food Safety Authority, 2018b;

European Food Safety Authority, 2018c; European Food Safety Authority,

2018d). The EU approved the ban on the neonicotinoids on April, 2018 and

clothianidin, imidacloprid, and thiamethoxam are expected to be totally banned

for all outdoor uses since the end of 2018 (Carrington, 2018).

Analysis of pesticide residues in apiculture samples

As numerous beekeepers and related industry have suffered from a decline in

honey bee population, researches have tried to find more accurate and

significant relationship between bee death incidents (including CCD) and

neonicotinoids as well as multiresidues by developing analytical method and

monitoring residue levels in various apiculture samples. Honey bee is one of

the complex matrix consisting of protein and fat. Pollen, which is one of the

bee products has high protein and sugar (Komosinska-Vassev et al., 2015). The

major composition of honey is sugar. Therefore, the challenge is to determine

pesticides without overlaps between target analytes and matrices from protein,

fat, and sugar. Introduction of mass spectrometry coupled to a liquid

chromatography or gas chromatography makes it possible to analyze many

pesticides without matrix interferences (Table 15). In particular, tandem mass

123

spectrometry allows high selective sensitive and analysis. Jovanov et al. (2013)

reported a trace level of detection (limit of detection; 0.5-1.0 ng/g) for seven

neonicotinoids in honey using LC-MS/MS (Jovanov et al., 2013). In addition,

tandem mass spectrometry enables a simultaneous analysis of hundreds of

target analytes. Many recent literature have developed multiresidue

methodology for more than two hundred pesticides in apiculture sample such

as honeybee and pollen (Vázquez et al., 2015; Kiljanek et al., 2016). With the

development of analytical technique, simple and convenient sample treatment

procedure such as the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and

Safe) is applicable in beekeeping samples (Wiest et al., 2011; Kasiotis et al.,

2014).


In this study, three neonicotinoids (clothianidin, imidacloprid, and

thiamethoxam) and 391 pesticides in honey bee (dead imago, healthy imago,

and larva), pollen, and honey were analyzed using LC-MS/MS or GC-MS/MS.

The modified QuEChERS method was used for treatments of bee, honey, and

pollen samples and the methodologies of the three neonicotinoids was fully

validated with parameters of limit of quantitation (LOQ), linearity of

calibration, and recovery. The residue levels of neonicotinoids and pesticide

multiresidues were determined in the samples collected near two areas of an

apple orchard and a pepper field to improve knowledge of honey bee exposure

and to carry out risk assessment for some suspicious pesticides using acute oral

LD50 of bee.

124

Table 15. Representative pesticide multiresidue analytical method in apiculture samples


Number of

analytes

Reference

1 Honey bee LC-MS/MS QuEChERS1) 200 (Kiljanek et al., 2016)

2 Pollen LC-MS/MS and

GC-MS/MS

QuEChERS 253 (Vázquez et al., 2015)

3 Honey bee, pollen, and wax GC-MS dSPE2) (Z-Sep3))

11 (Li et al., 2015)

4 Honey bee, pollen, and honey LC-MS/MS QuEChERS 115 (Kasiotis et al., 2014)

5 Honey LC-MS/MS DLLME4) 7 (Jovanov et al., 2013)

6 Pollen and nectar LC-MS/MS SPE5) 12 (Dively and Kamel, 2012)

7 Honey bee, pollen, bee bread, nectar, and honey

LC-MS/MS QuEChERS 5 (Pohorecka et al., 2012)

8 Honey bee LC-MS/MS SPE 5 (Martel and Lair, 2011)

9 Honey bee, pollen, and honey LC-MS/MS and

GC-TOF6)

QuEChERS 80 (Wiest et al., 2011)

10 Honey bee, pollen, and honey LC-MS/MS QuEChERS +SPE

12 (Kamel, 2010)

125



Number of

analytes

Reference

11 Honey GC-MS SPE 48 (Rissato et al., 2007)

12 Honey LC-MS/MS OCLLE7) 17 (Pirard et al., 2007)

13 Pollen LC-MS/MS SPE and LLE8) 41 (Chauzat et al., 2006)

1)Quick, Easy, Cheap, Effective, Rugged, and Safe

2)Dispersive solid-phase extraction

3)Zirconium based sorbent

4)Dispersive liquid-liquid microextraction

5)Solid-phase extraction

6)Time-of-flight

7)On-column liquid-liquid extraction

8)Liquid-liquid extraction

126



Reference standards of clothianidin (purity; 99.6%), imidacloprid (99.5%), and

thiamethoxam (99.0%) were obtained from Wako Pure Chemical Industries

(Osaka, Japan), ChemService (West Chester, PA), and Dr. Ehrenstorfer

(Augsburg, Germany), respectively. Pesticide standards (>98%) and stock

solutions (1,000 mg/L) for multiresidue analysis were purchased from Wako

Pure Chemical Industries, ChemService, Dr. Ehrenstorfer, Sigma-Aldrich (St.

Louis, MO), Tokyo Chemical Industry (Tokyo, Japan), AccuStandard (New

Haven, CT), and ULTRA Scientific (North Kingstown, RI, USA). Formic acid

(LC-MS grade) and ammonium formate (≥99.0%) magnesium sulfate

anhydrous (MgSO4, ≥99.5%), sodium acetate anhydrous (NaOAc, ≥99.0%),

sodium citrate dibasic sesquihydrate (Na2HCitr·1.5H2O, ≥99.0%), and sodium

citrate tribasic dihydrate (Na3Citrate·2H2O, ≥99.0%) was sourced from Sigma-

Aldrich. Solvents (acetonitrile, acetone, and methanol) for HPLC grade was

bought from Fisher Scientific (Seoul, Republic of Korea). Sodium chloride

(NaCl, 99.0%) was purchased from Samchun (Gyeonggi-do, South Korea).

QuEChERS extraction packet (4 g MgSO4, 1 g NaCl, 1 g Na3Citrate·2H2O, 0.5

g Na2HCitr·1.5H2O) was purchased from ULTRA Scientific. Dispersive SPE in

a 2-mL microcentrifuge tube (150 mg MgSO4 and 25 mg PSA) was obtained

from Agilent Technologies (Santa Clara, CA). Ceramic homogenizers for 15-

mL tube were purchased from Agilent Technologies.

127

Preparation of matrix-matched standards

Stock solutions (≤1,000 mg/L) was prepared from reference standards using

acetonitrile, acetone, and methanol. Aliquots of solutions for clothianidin,

imidacloprid, and thiamethoxam were mixed to give a concentration of 10 mg/L.

A portion of each multiresidual pesticide were also mixed to make a

concentration of 2.5 mg/L. Each standard mixture was further diluted with

acetonitrile, respectively. Standard solutions were stored at -20°C before use.

Matrix-matched standards were prepared by mixing 0.1 mL standard solutions

and 0.4 mL of the blank matrix solutions of bee, pollen, and honey.

Sample collection

Blank bee samples without pesticides were thankfully obtained from

beekeepers of Seoul National University (the Republic of Korea) and blank

pollen and honey were purchased from a commercial market. Bee (Apis

mellifera L.) colonies for the field monitoring were obtained from the

beekeepers near monitoring areas. The colonies (n = 5) were placed in Giran

(three monitoring sites; Geumgok, Mukgye, and Odae, the areas of apple

orchards) and Yeongyang (two sites; Sanun and Daecheon, the areas of pepper

fields), the Republic of Korea, respectively (Fig. 23). Before the investigation,

all colonies were treated with fluvalinate to control mites and additional

pesticide treatments around colonies were not conducted during the

investigation.

128

Fig. 23. Distribution of monitoring sites in the Republic of Korea

130

The monitoring periods for dead imago and pollen were at

approximately 7 days intervals between before and after full bloom seasons of

apple (from April 24 to June 6, 2014 in Giran) and pepper (from July 6 to

August 6, 2014 in yeongyang), respectively (Table 16 and 17). In each area,

baskets and pollen traps were installed in front of the five colony entries to

collect dead imagos and pollen, respectively. Healthy imagos, larvae (including

the pupal stages), and honey were collected near or in colonies on June 10 in

Giran and August 7 in Yeongyang, respectively. All samples were stored at -

20 °C until sample treatment and analysis. The dead and pollen samples from

5 different colonies in each day investigation were combined together, resulting

in one dead imago and pollen sample per apiary, respectively. The healthy, larva,

and honey samples collected from each colony were collected and analyzed

respectively.

131

Table 16. Sampling results in Giran during investigation period on April 24 to June 6, 2014

Sample Site (Giran)

Monitoring data, 2014 21-Apr

25-Apr

3-May

10-May

17-May

24-May

30-May

6-Jun

10-Jun, colony No. 1

No. 2

No. 3

No. 4

No. 5

Dead imago

Geumgok O O O O O O O O Mukgye O O O O O O O O

Odae O O O O O O O O Healthy imago

Geumgok O O O O O

Mukgye O O O O O

Odae O O O O O

Larva Geumgok O O O O

Mukgye O O O Odae O O

Pollen Geumgok O O O O O O O O Mukgye O O O O O O O O

Odae O O O O O O O O Honey Geumgok O O O O O

Mukgye O O O O O

Odae O O O O O

132

Table 17. Sampling results in Yeongyang during investigation period on July 6 to August 7, 2014

Sample Site (Yeongyang)

Monitoring data, 2014 6-Jul 14-Jul 20-Jul 27-Jul 1-Aug 6-Aug 7-Aug, colony

No. 1 No. 2 No. 3 No. 4 No. 5 Dead imago

Sanun O O O O O O Daecheon O O O O O O

Healthy imago

Sanun O O O O O

Daecheon O O O O O

Larva Sanun O O O O O

Daecheon O O O O O

Pollen Sanun O O O O O Daecheon O O O O O

Honey Sanun O O O O O

Daecheon O O O O O

133

Instrumental conditions of LC-MS/MS and GC-MS/MS

LC-MS/MS. For the determination of three neonicotinoids (clothianidin,

imidacloprid, and thiamethoxam) and multiresidual pesticides selected to an

LC analysis, a Shimadzu LCMS-8040 triple quadrupole mass spectrometer

coupled to a Shimadzu Nexera UHPLC (Kyoto, Japan) was utilized. The

UHPLC system was comprised of a degasser (DGU-20A5), two of solvent

delivery module (LC-30AD), an autosampler (SIL-30AC), and a column oven

(CTO-20A). These instruments were connected by communications bus

module (CBM-20A).

For the MS/MS conditions, nebulizing gas and drying gas flow rates

were 3 L/min and 15 L/min, respectively. Desolvation line (DL) and heat block

temperature values were 250 °C and 400 °C, respectively. A polarity switching

electrospray ionization (ESI) mode was employed for an ionization of target

analytes. Argon gas (≥99.999%) was used in collision-induced dissociation

(CID) during a product scan or multiple reaction monitoring (MRM). Auto

dwell time allocation was adopted and detection window for each pesticide was

±1.0 min. LabSolution version 5.60 was utilized as a LCMS software during

data processing.

For the UHPLC conditions during analysis of three neonicotinoids in

bee, and pollen, the separation was performed on a Luna C18 column (100 ×

2.0 mm, 3 µm, Phenomenex, Torrance, CA) coupled with SecurityGuard Ultra

guard column (Phenomenex) at 40 °C oven temperature. Mobile phases were

0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B), and the

total flow rate was 0.2 mL/min. The gradient program for mobile phases B was

initialized at 5% for 0.5 min, ramped to 95% for 2 min, held at 95% for 1.5 min,

134

and then raised to 100% for 0.5 min. After the elution, the percentage of B was

sharply reduced to 5% for 0.5 min and held at 5% for 2 min for initialization of

the mobile phases. Total of the runtime was 7 min and the injection volume was

5 μL.

For the UHPLC conditions during analysis of three neonicotinoids in

honey and pesticide multiresidues in bee, pollen, and honey, the separation was

conducted on a Kinetex C18 column (100 × 2.1 mm, 2.6 µm, Phenomenex,

Torrance, CA) coupled with SecurityGuard Ultra guard column at 40 °C oven

temperature. Mobile phases were 5 mM ammonium formate and 0.1% formic

acid in water (A) and 5 mM ammonium formate and 0.1% formic acid in

acetonitrile (B), and the total flow rate was 0.2 mL/min. The gradient program

for mobile phases B was initialized at 5% for 0.5 min, ramped to 95% for 6.5

min, held at 95% for 3 min, and then raised to 100% for 0.5 min. After the

elution, the percentage of B was sharply reduced to 5% for 1 min and held at

5% for 3 min for initialization of the mobile phases. Total of the runtime was

15 min and the injection volume was 5 μL. Deionized water was prepared in

house using LaboStar TWF UV 7 (Siemens, MA).

GC-MS/MS. For the determination multiresidual pesticides selected to a GC

analysis, a Bruker SCION TQ triple quadrupole mass spectrometer coupled to

a Bruker SCION 451 GC gas chromatograph (Billerica, MA) was utilized. The

GC was furnished with an autosampler (CP-8400, Bruker). In the GC, a Zebron

ZB-SemiVolatiles (30 m × 0.25 mm i.d., 0.25 μm df, Phenomenex) capillary

column was installed. Helium (≥99.999%) was used as a carrier gas and total

constant flow rate was 1.0 mL/min. The injection mode was splitless with a

135

pulsed pressure at 40 psi, and inlet temperature was 260 °C. The oven

temperature was initialized at 90 °C for 3 min, ramped to 150 °C (20 °C/min),

raised 300 °C (5 °C/min), and then held at 300 °C for 4 min. The total run time

was 40 min and the injection volume was 2 μL. For the MS/MS conditions,

transfer line, manifold, and ion source temperature values were 280, 40, and

230 °C, respectively. The electron ionization (EI) mode at 70 eV was employed

for an ionization of target analytes. Argon gas (≥99.999%) was used in

collision-induced dissociation (CID) and the collision pressure was 1.50 mTorr.

The Dynamic mode (EDR) was used for the detector signal gain. MS

Workstation (version 8.2) was utilized as a software during data processing.

MRM optimization in LC-MS/MS and GC-MS/MS

Each standard solution at 1-10 mg/L was injected into an instrument to obtain

a full scan spectrum (m/z 50-1,000 for LC-MS/MS and m/z 50-500 for GC-

MS/MS). For LC-MS/MS, a quasi-molecular ion (e.g., [M+H]+, [M-H]-) was

selected as a precursor ion. The precursor ion was subjected to a product scan

under CID with various collision energies (CE). From a product scan spectrum,

two product ions were chosen in a consideration of selectivity and sensitivity.

For GC-MS/MS, one molecular ion, or fragment ions were selected as a

precursor ion(s) and the selection of product ion followed the procedures

described above. Among the two MRM transitions, one was appointed as a

quantifier and the other as a qualifier. Using the established MRM conditions,

retention time (tR) for each pesticide was verified.

136

Sample preparation

Bee (dead imago, healthy imago, and larva) was homogenized with dry ice

using a mini blender. The QuEChERS EN 15662 (EN 15662, 2008) procedure

was modified as the sample amounts. For bee and pollen, 2 g of aliquots in a

15-mL centrifuge tube was treated with 2 mL (bee) or 5 mL (pollen) of water,

respectively. The sample was left for approximately 15 min to let the sample

absorb the water entirely. A ceramic homogenizer and acetonitrile (2 mL) were

added to the sample and the tube was shaken for 10 min at 300 rpm. The extract

was treated with MgSO4 (0.8 g) NaCl (0.2 g), Na3Citrate·2H2O (0.2 g), and

Na2HCitr·1.5H2O (0.1 g), and then shaken vigorously for 1 min. During the

entire partitioning procedures, the sample was cooled down with an ice bath.

The tube was centrifuged for 10 min at 3,000 rpm, and 1 mL of the upper layer

was added into a 2-mL microcentrifuge tube containing 150 mg MgSO4 and 25

mg PSA (dSPE) under an ice bath. After the sample was mixed for 1 min using

a vortex mixer, and then centrifuged for 5 min at 13,000 rpm. The upper layer

(0.4 mL) was matrix-matched with 0.1 mL acetonitrile.

For a honey sample, 5 g of aliquots in a 50-mL centrifuge tube was

treated with 10 mL water, extracted with 5 mL of acetonitrile, and then

partitioned with a QuEChERS extraction packet (4 g MgSO4, 1 g NaCl, 1 g

Na3Citrate·2H2O, and 0.5 g Na2HCitr·1.5H2O). The organic layer (1 mL) was

treated with dSPE and centrifuged, and then the upper layer (0.4 mL) was

matrix-matched with 0.1 mL acetonitrile.

Each sample of bee, pollen, and honey was equivalent to 0.8 g per 1 mL

of the final extract. The final extract was divided into four 2-mL amber vials

137

(two for neonicotinoid and two for pesticide multiresidue analysis) and injected

into the LC-MS/MS (5 μL) or GC-MS/MS (2 μL), respectively.

Method validation for clothianidin, imidacloprid, and thiamethoxam

Analytical methods for neonicotinoids in bee, pollen, and honey were subjected

to validation with parameters of the limit of quantitation (LOQ), linearity of

calibration, and recovery in bee, pollen, and honey samples. The LOQ was

determined by selecting the lowest level of the concentrations from matrix-

matched standards satisfying signal to noise ratio (S/N) ≥10 (De Bièvre et al.,

2005). The linearity of calibration was investigated using matrix-matched

standards with linear ranges of 1-50 ng/g. A weighting regression factor (1/x)

was employed to correct quantitation near lower concentrations of the

calibration curve. The linearity of each calibration curve was expressed as the

correlation coefficient (r2). The recovery test was conducted at treated levels of

1, 5, and 10 ng/g. Each sample was spiked with neonicotinoids and prepared as

the procedures described above (n = 3). The LC-MS/MS responses from

recovery samples were compared with those of matrix-matched standard to

calculate recovery rates of neonicotinoids.

Pesticide multiresidue screening in bee, pollen, and honey

Pesticide screening and quantitation for 391 pesticides (205 for LC-MS/MS and

186 for GC-MS/MS) were conducted. The matrix-matched standard calibration

was employed to correct a matrix effect (a phenomenon in which a signal

intensity is enhanced or suppressed by matrices on the LC- and GC-MS/MS).

Linear range was 1-200 ng/g for bee and pollen, and 1-500 ng/g for honey and

138

the weighting regression factor (1/x) for each pesticide calibration was applied

during residue determination.

Statistical analysis

The percentile method was utilized to summarize residue dataset (Kiljanek et

al., 2016). For a few suspicious residue values (e.g., exceeding LD50), outlier

test was conducted with the Dixon’s Q test to determine whether the values are

statistically acceptable. The values <LOQ were assigned to 1/2 LOQ of each

pesticide (Jaraczewska et al., 2006; Mercadante et al., 2013). The difference

was considered to be significant if p value was less than 0.05 (p <0.05).

Safety information




implemented.

139


Body weights of honey bees

During the monitoring period, a total of 8,325 honey bee imagos (6,615 for the

dead and 1,710 for the healthy) was collected and weighted (Table 18). The

total average of body weights in Giran was higher than that in Yeongyang in

both statuses. The total averages of bee body weights were 0.06 g/bee for the

dead and 0.11 g/bee for the healthy. Zoltowska et al. (2011) reported the body

weights of newly emerged bee workers, average 0.1 g, similar to our results

(Zoltowska et al., 2011). The average body weight of dead bees was 55% of

that of healthy imagos due to dehydration. Therefore, each measured value from

dead or healthy imago sample was corrected by the average body weight (bw)

of the healthy imago (0.11 g) to reduce an over- or underestimation, as

following equation:

Residue level (𝑎𝑎𝑛𝑛 ∙ 𝑛𝑛𝑏𝑏𝑤𝑤−1) =Measured value in a sample (𝑎𝑎𝑛𝑛 ∙ 𝑛𝑛−1) × 𝐴𝐴

Total average of healthy bee bw (𝑛𝑛 ∙ 𝑏𝑏𝑠𝑠𝑠𝑠𝑏𝑏𝑤𝑤)

where: 𝐴𝐴 = average of dead bee bw in corresponding date (𝑛𝑛 ∙ 𝑏𝑏𝑠𝑠𝑠𝑠𝑏𝑏𝑤𝑤)

140

Table 18. The numbers of dead and healthy imago collected in the two areas and their total and average body weights

Status1) Dead imago Healthy imago

Area/site No. of Bees (n)

Total body weight

(g)

Average body weight

(g/bee)

No. of Bees (n)

Total Body weight

(g)

Average body weight

(g/bee)

Giran Geumgok 1,069 94.86 0.09 384 47.04 0.12

Mukgye 1,480 68.36 0.05 315 38.88 0.12

Odae 2,053 147.96 0.07 318 37.56 0.12

Total 4,602 311.18 0.07 1,017 123.48 0.12

Yeongyang Sanun 1,448 73.44 0.05 381 37.88 0.10

Daewon 565 37.07 0.07 312 33.50 0.11

Total 2,013 110.51 0.05 693 71.38 0.10

Total 6,615 421.69 0.06 1,710 194.86 0.11

1)The weights of the larvae were not measured.

141

MRM optimization

The MRM profiles of the three neonicotinoids (clothianidin, imidacloprid, and

thiamethoxam) were optimized on LC-MS/MS. The precursor ions for the

compounds were ionized positively by adducting proton ([M+H]+). With these

precursor ions, product ions under optimum CE were selected. Because of

different LC conditions, retention times (tR) in honey samples were slightly

faster than those in bee and pollen samples. The detailed MRM transitions and

tR were in Table 19. The MRM profiles of 391 pesticides were also successfully

established using LC-MS/MS (205 compounds) or GC-MS/MS (186

compounds). The detailed MRM transitions and retention times on LC- and

GC-MS/MS were in Table S1 and S2.

142

Table 19. The established retention times (tR), monoisotopic masses, quasi-molecular ion types, and MRM transitions of LC-

MS/MS for the neonicotinoid pesticides

Matrix Neonicotinoid tR (min)

Mono Isotopic

mass

Quasi-molecular ion

Precursor ion > Product ion (CE, eV)

Quantification Identification

Bee and

pollen

Clothianidin 3.36 249 [M+H]+ 250 > 169 (-12) 250 > 132 (-16)

Imidacloprid 3.41 255 [M+H]+ 256 > 209 (-14) 256 > 175 (-19)

Thiamethoxam 3.23 291 [M+H]+ 292 > 211 (-12) 292 > 181 (-22)

Honey Clothianidin 3.19 249 [M+H]+ 250 > 169 (-12) 250 > 132 (-16)

Imidacloprid 3.23 255 [M+H]+ 256 > 209 (-14) 256 > 175 (-19)

Thiamethoxam 3.09 291 [M+H]+ 292 > 211 (-12) 292 > 181 (-22)

143

Method validation for neonicotinoids

For the three neonicotinoids (clothianidin, imidacloprid, and thiamethoxam),

analytical methods in bee, pollen, and honey were validated. The validation

parameters were the LOQ, linearity of calibration, and recovery. The LOQ of

clothianidin, imidacloprid, and thiamethoxam was 1 ng/g, respectively, in all

matrices (Table 20). Because the average body weight of healthy imagos was

0.11 g (see Table 18), the minimum of 0.11 ng per a bee can be detectable for

neonicotinoids. The acute oral (LD50) toxicities for clothianidin, imidacloprid,

and thiamethoxam are 3.79, 3.7, and 5 ng/bee (acute contact LD50; 44.3, 81, and

24 ng/bee), respectively (European Commission, 2005; European Commission,

2006; European Food Safety Authority, 2008). These values were 33.6-45.5

times (acute contact LD50; 306-413 times) higher than LOQ (0.11 ng bw /bee),

thus the sensitivity in the methodology is sufficiently low for the

ecotoxicological risk assessment. The correlation coefficients (r2) for

neonicotinoids were greater than 0.990 in all matrices, showing excellent

linearity of calibration.

The recovery ranges for the neonicotinoids were 74.4-98.2% (RSD 0.9-

17.0%) in bee, 79.9-102.5% in pollen (RSD 0.9-14.4%) and 78.0-116.2% (RSD

0.4-17.1%) in honey at treated levels of 1, 5, and 10 ng/g, respectively.

According to the guidance of SANTE/11813/2017, the acceptable criteria for

recovery is 70-120% with RSD ≤20% (European Commission, 2017). All the

recovery results fell within the criteria, thus these analytical methods for

neonicotinoids had the reliable trueness and precision including the LOQ level

in bee, pollen, and honey.

144

Table 20. The limit of quantitation (LOQ), correlation coefficients (r2), recovery results for neonicotinoid pesticides in bee,

pollen, and honey samples

Matrix Neonicotinoid LOQ ng/g

r2 Recovery, % (RSD, %)

1 ng/g 5 ng/g 10 ng/g

Bee Clothianidin 1 0.9992 74.4 (3.5) 97.1 (4.4) 92.7 (1.3)

Imidacloprid 1 0.9963 94.9 (17.0) 91.0 (5.6) 94.8 (8.1)

Thiamethoxam 1 0.9985 92.9 (5.9) 98.2 (6.3) 94.2 (0.9)

Pollen Clothianidin 1 0.9981 94.0 (14.4) 79.9 (3.9) 81.1 (6.2)

Imidacloprid 1 0.9962 102.5 (11.3) 95.4 (9.0) 90.9 (5.4)

Thiamethoxam 1 0.9983 90.5 (7.4) 95.0 (3.9) 94.8 (0.9)

Honey Clothianidin 1 0.9962 78.0 (17.1) 100.4 (6.2) 105.3 (6.8)

Imidacloprid 1 0.9964 111.4 (4.8) 106.2 (8.0) 90.9 (2.9)

Thiamethoxam 1 0.9920 85.1 (14.5) 116.2 (0.4) 114.2 (4.5)

145

Analysis of neonicotinoids (clothianidin, imidaclprid, and thiamethoxam)

in bee, pollen, and honey

Five field sites were investigated (three in Giran and two in Yeongyang) on

April 24 to June 10, 2014 (Giran), and on July 6 to August 7, 2014 (Yeongyang).

The average foraging distance of honey bees from a bee colony is about 2 km

(Visscher and Seeley, 1982). Therefore, the distances between the areas were at

least 5 km not to overlap the spheres of activities of honey bee workers from

different areas (see Fig. 23). The monitoring periods were between before and

after full bloom seasons of apple in Giran or pepper in Yeongyang so that bee

workers could sufficiently pollinate apple or pepper and do foraging activities.

There has been a guidance for applications of clothianidin, imidacloprid, and

thiamethoxam in apple and pepper (Korea Crop Protection Association, 2012;

Korea Crop Protection Association, 2015). It is expected that these

neonicotinoid ingredients were conventionally sprayed on apple orchards in

Giran and on pepper fields in Yeongyang. The total numbers of available

samples collected during the monitoring periods were 36 (dead imago), 25

(healthy imago), 19 (larva), 34 (pollen), and 25 (honey), respectively, and these

samples were analyzed and residues of neonicotinoids were determined.

Bee. The residue concentrations from measured values for bee imago samples

were corrected by the average body weight (bw) of the healthy imago (0.11 g)

to reduce an over- or underestimation, as the equation described above.

In Giran, the area of apple orchards, at least one of the three

neonicotinoids were detected positively in 15 (62.5%) of the 24 dead imago

samples (Table 21). Among the three sites (Geumgok, Mukgye, and Odae) in

146

Giran, the smallest detection frequency was observed in Odae (3 samples, 37.5%

of the total). The narrowest determination range within 75th to 95th percentile

was observed in Geumgok (0.8-4.9 ng/g bw) and the highest residue was found

in Odae (15.3 ng/g bw, clothianidin). When the results are sorted by ingredients,

clothianidin showed the largest detection frequency (11 samples, 45.8% of the

total) and the highest residue range (2.9 to 13.5 ng/g bw) within 75th and 95th at

the three sites. On the other hand, thiamethoxam showed the lowest residues in

the dead among the three neonicotinoids. No neonicotinoid was detected in

healthy imago and larva samples.

In Yeongyang, the area of pepper fields, at least one of the three

neonicotinoids were detected in 10 (83.3%) of 12 dead imago samples (Table

22). The two sites (Sanun and Daecheon) in Yeongyang exhibited the same

detection frequencies (5 samples, 83.3% of the total, respectively).

Determination ranges within 50th to 95th percentile were lower in Sanun (0.8-

6.1 ng/g bw) than in Daecheon (1.5-22.4 ng/g bw) and the highest residue was

found in Daecheon (56.9 ng/g bw, clothianidin). When the results are sorted by

ingredients, clothianidin showed the largest detection frequency (9 samples,

75.0% of the total) and the highest residue range (3.0 to 34.6 ng/g bw) within

50th and 95th at the two sites. No neonicotinoid was detected in healthy imago

and larva samples.

147

Table 21. Distribution of neonicotinoid residues in dead imago at three sites in Giran

Dead imagos in Giran (No. of the total samples)

Frequency of positive

detection (%)

Min ng/g bw

Percentile, ng/g bw Max ng/g bw

50th 75th 90th 95th

Total (24)

15 (62.5%)

<LOQ <LOQ 0.9 3.5 10.4 15.3

Sorted by

area

Geumgok (8)

6 (75.0%)

<LOQ <LOQ 0.8 3.1 4.9 9.1

Mukgye (8)

6 (75.0%)

<LOQ <LOQ 0.9 1.4 10.9 13.7

Odae (8)

3 (37.5%)

<LOQ <LOQ 0.7 4.6 11.0 15.3

Sorted by

ingredient

Clothianidin (24)

11 (45.8%)

<LOQ <LOQ 2.9 11.5 13.5 15.3

Imidacloprid (24)

6 (25.0%)

<LOQ <LOQ 0.6 1.6 3.4 11.9

Thiamethoxam (24)

3 (12.5%)

<LOQ <LOQ <LOQ 0.6 1.2 1.6

148

Table 22. Distribution of neonicotinoid residues in dead imago at two sites in Yeongyang

Dead imagos in Yeongyang (No. of the total samples)


detection (%)

Min ng/g bw

Percentile, ng/g bw Max ng/g bw

50th 75th 90th 95th

Total (12)

10 (83.3%)

<LOQ 1.0 5.1 7.9 15.0 56.9

Sorted by

area

Sanun (6)

5 (83.3%)

<LOQ 0.8 2.7 5.7 6.1 6.9

Daecheon (6)

5 (83.3%)

<LOQ 1.5 6.4 15.0 22.4 56.9

Sorted by

ingredient

Clothianidin (12)

9 (75.0%)

<LOQ 3.0 5.5 15.4 34.6 56.9

Imidacloprid (12)

6 (50.0%)

<LOQ <LOQ 3.1 8.6 11.4 14.5

Thiamethoxam (12)

7 (58.3%)

<LOQ 0.8 3.5 5.6 5.7 5.9

149

The positive detection ratio per a sample in Yeongyang was 1.3 times

higher than that in Giran. In accordance with the statistics in Table 21 and 22,

clothianidin showed higher residues in all percentile parameters and maximum

values than thiamethoxam and imidacloprid. Thiamethoxam is easily converted

into clothianidin in insects and plants metabolism (Nauen et al., 2003), thus it

is possible that thiamethoxam was biotransformed rapidly into clothianidin by

bee, apple, pepper, or other biotas.

To evaluate ecotoxicology, neonicotinoid residues were compared to

bee acute oral LD50. Because the average bodyweight of healthy imago was

0.11 g (see Table 18), Therefore, LD50s of 3.79, 3.7, and 5 ng/bee for

clothianidin, thiamethoxam, and imidacloprid (European Commission, 2005;

European Commission, 2006; European Food Safety Authority, 2008)

correspond with 34.5, 33.6, and 45.5 ng/g bw, respectively.

In Giran, residues of three neonicotinoids in the dead imago samples

were under LD50 values (Fig. 24) during the investigation period. The

Maximum residues of neonicotinoids were 44.3% (clotianidin), 35.4%

(imidacloprid), and 3.5% (thiamethoxam) of LD50, respectively. In Yeongyang,

residues of neonicotinoids in the dead imago samples were under LD50 values

except for clothianidin (Fig. 25). The Maximum residues of neonicotinoids

were 164.9% (clotianidin), 43.2% (imidacloprid), and 13.0% (thiamethoxam)

of LD50, respectively. The only one sample collected on June 20 showed

exceeding LD50 of clothianidin 56.9 ng/g bw. For this value, the Dixon’s Q test

was conducted and it was significant outlier (p <0.05). Therefore, the effects of

neonicotinoid residues in bee were not lethal, considering individual toxicity

only.

150


(c) thiamethoxam in dead imago samples at three sites in Giran

152


(c) thiamethoxam in dead imago samples at two sites in Yeongyang

154

Pollen. In Giran, at least one of the three neonicotinoids were detected

positively in 14 (58.3%) of the 24 pollen samples (Table 23). Among the three

sites (Geumgok, Mukgye, and Odae) in Giran, the smallest detection frequency

was observed in Geumgok (3 samples, 37.5% of the total), whereas the highest

residue level was found at this site (17.0 ng/g, thiamethoxam). The lowest

residue range within 75th to 95th percentile was observed in Odae (<LOQ to 3.1

ng/g). When the results are sorted by ingredients, imidacloprid showed the

largest detection frequency (8 samples, 33.3% of the total). Thiamethoxam

showed the lowest residue range within 75th to 95th percentile (<LOQ to 0.9

ng/g), whereas indicated the highest residue level (17.0 ng/g) among the

neonicotinoids.

In Yeongyang, samples collected on July 14 in both of Sanun and

Daecheon were not able to be measured due to insufficient sample amounts. At

least one of the three neonicotinoids were detected positively in 5 (62.5%) of

the 8 pollen samples (Table 24). The percentile values were similar in Sanun

and Daecheon. When the results are sorted by ingredients, imidacloprid showed

the largest detection frequency (4 samples, 50.0% of the total), the highest

percentile values (1.4-4.3 ng/g within 50th to 95th percentile), and the highest

maximum residue (4.5 ng/g) among the neonicotinoids. In contrast, clothianidin

exhibited the smallest detection frequency (1 sample, 12.5% of the total), the

lowest percentile ranges (<LOQ to 0.9 ng/g within 50th to 95th percentile), and

the lowest maximum residue (1.1 ng/g) among the neonicotinoids.

155

Table 23. Distribution of neonicotinoid residues in pollen at two sites in Giran

Pollen in Giran (No. of the total samples)


detection (%)

Min ng/g

Percentile, ng/g Max ng/g

50th 75th 90th 95th

Total (24)

14 (58.3%)

<LOQ <LOQ <LOQ 2.3 6.3 17.0

Sorted by

area

Geumgok (8)

3 (37.5%)

<LOQ <LOQ <LOQ 3.8 15.0 17.0

Mukgye (8)

6 (75.0%)

<LOQ <LOQ 1.0 1.8 4.8 7.6

Odae (8)

5 (62.5%)

<LOQ <LOQ <LOQ 2.3 3.1 8.2

Sorted by

ingredient

Clothianidin (24)

7 (29.2%)

<LOQ <LOQ 1.2 4.5 5.3 8.2

Imidacloprid (24)

8 (33.3%)

<LOQ <LOQ 1.2 2.1 6.8 16.8

Thiamethoxam (24)

2 (8.3%)

<LOQ <LOQ <LOQ <LOQ 0.9 17.0

156

Table 24. Distribution of neonicotinoid residues in pollen at two sites in Yeongyang

Pollen in Yeongyang (No. of the total samples)


detection (%)

Min ng/g

Percentile, ng/g Max ng/g

50th 75th 90th 95th

Total (8)

5 (62.5%)

<LOQ <LOQ 1.2 2.4 3.6 4.5

Sorted by

area

Sanun (4)

2 (50.0%)

<LOQ <LOQ 0.8 2.4 3.4 4.5

Daecheon (4)

3 (75.0%)

<LOQ <LOQ 1.3 2.2 3.0 3.8

Sorted by

ingridient

Clothianidin (8)

1 (12.5%)

<LOQ <LOQ <LOQ 0.7 0.9 1.1

Imidacloprid (8)

4 (50.0%)

<LOQ 1.4 2.8 4.0 4.3 4.5

Thiamethoxam (8)

2 (25.0%)

<LOQ <LOQ 0.8 1.6 1.6 1.7

157

The positive detection ratio per a sample was similar in both of Giran

and Yeongyang. In accordance with the statistics in Table 23 and 24,

imidacloprid showed higher residues in all percentile parameters and maximum

values than clothianidin and thiamethoxam. To evaluate ecotoxicology of bee,

neonicotinoid residues in pollen were compared to bee acute oral LD50 values.

In Giran, residue levels of three neonicotinoids were under LD50s (Fig. 26). The

Maximum residues of neonicotinoids were 23.8% (clotianidin), 50.0%

(imidacloprid), and 37.4% (thiamethoxam) of LD50 values, respectively. In

Yeongyang, residue concentrations of neonicotinoids were also under LD50 (Fig.

27). The Maximum residues of neonicotinoids were 3.2% (clotianidin), 13.4%

(imidacloprid), and 3.7% (thiamethoxam) of LD50 values, respectively.

Therefore, the effects of neonicotinoid residues in pollen were not lethal to bees,

considering individual toxicity only.

158


(c) thiamethoxam in pollen samples at three sites in Giran

160


(c) thiamethoxam in pollen samples at two sites in Yeongyang

162

Honey. In Giran, residue ranges of clothianidin and imidacloprid were <LOQ-

1.4 ng/g and <LOQ-1.3 ng/g, respectively, and thiamethoxam was <LOQ

(Table 25). In Yeongyang, residue ranges of imidacloprid and thiamethoxam

were <LOQ-2.6 ng/g and <LOQ-1.9 ng/g, respectively, and clothianidin was

<LOQ. These residue levels were ≤7.7% of the three neonicotinoid LD50s.

Therefore, the effects of neonicotinoid residues in honey were not lethal to bees,

considering individual toxicity only. The residue ranges were also lower than

the MRLs of three neonicotinoids (0.05 mg/kg, respectively), accounting for

≤5.2% of the MRLs. This indicate that honey produced in these area is

sufficiently safe in aspect of human health and available for a food stuff.

163

Table 25. Distribution of neonicotinoid residues in honey in Giran and

Yeongyang

Giran

Date Neonicotinoid

10-Jun, Colony (ng/g) No.1 No.2 No.3 No.4 No.5

Geumgok

Clothianidin <LOQ 1.4 <LOQ 1.3 <LOQ Imidacloprid <LOQ <LOQ <LOQ <LOQ <LOQ

Thiamethoxam <LOQ <LOQ <LOQ <LOQ <LOQ Mukgye

Clothianidin <LOQ <LOQ 1.4 <LOQ <LOQ Imidacloprid <LOQ <LOQ <LOQ <LOQ <LOQ

Thiamethoxam <LOQ <LOQ <LOQ <LOQ <LOQ Odae

Clothianidin <LOQ <LOQ <LOQ <LOQ <LOQ Imidacloprid 1.1 <LOQ <LOQ 1.3 <LOQ

Thiamethoxam <LOQ <LOQ <LOQ <LOQ <LOQ Yeongyang Date

Neonicotinoid 7-Aug, Colony (ng/g)

No.1 No.2 No.3 No.4 No.5 Sanun Clothianidin <LOQ <LOQ <LOQ <LOQ <LOQ

Imidacloprid <LOQ <LOQ <LOQ <LOQ <LOQ Thiamethoxam <LOQ <LOQ 1.9 1.3 1.1

Daecheon Clothianidin <LOQ <LOQ <LOQ <LOQ <LOQ Imidacloprid 1.9 <LOQ <LOQ 1.1 2.6

Thiamethoxam <LOQ <LOQ <LOQ <LOQ <LOQ

164

Analysis of pesticide multiresidues in bee, pollen, and honey

To evaluate variable ecotoxicological effects as well as neonicotinoids, 391

pesticides in bee, pollen, and honey samples were screened using MRM mode

of LC- and GC-MS/MS. The samples to be measured were the same as the

samples for the neonicotinoids analysis. Among the target pesticides, 52

analytes were positively detected in at least one of bee (dead imago, healthy

imago, and larva), pollen, and honey samples. Fluvalinate, its numbers of

detection frequencies are the largest among the pesticides in both of Giran (49

of the 87 samples) and Yeongyang (30 of the 52 samples), is an acaricide that

was treated in bee colonies before the investigation to control mites.

Diphenylamine is a post-harvest deterioration inhibitor for apple or a naturally

producted compound in some crops (Drzyzga, 2003). Atrazine, ethiofencarb

were not registered to application in apple and pepper as well as in any crops

neither, and the others were banned or suspended from sales recently or seemed

to come during treatment of other crops (Korea Crop Protection Association,

2013).

In Giran, the area of apple orchard, 46 pesticides were determined above

LOQ and 38 (82.6%) of them have been acceptable to treat on apple orchard in

the Republic of Korea (Table 26) (Korea Crop Protection Association, 2012;

Korea Crop Protection Association, 2015). Because these pesticides also have

been registered in various crops, some of the pesticides might come from other

crop residues. Fluvalinate (pyrethroid), etofenprox (pyrethroid), carbaryl

(carbamate), acetamiprid (neonicotinoid), and spiromesifen (tetronic acid)

ranked first to fifth among the pesticides in detection frequencies in this area

(Fig. 28). Etofenprox and spiromesifen were not determined in honey samples

165

and acetamiprid in honey samples showed larger detection ratio than the others.

Acetamiprid and etofenprox exhibited lower toxicity (acute oral LD50 14,500

and 270 ng/bee, corresponding with 132,000 and 2,500 ng/g bw in this study)

than neonicotinoids (thiamethoxam; 5 ng/bee), but highly toxic to bee (World

Health Organization; Tomlin, 2009). The residue levels for these pesticides are

lower than acute oral LD50s in all samples (Table 27). Fluvalinate and

spiromesifen are not hazardous to bee (acute oral LD50 163,000 ng/bee;

1,480,000 ng/g bw and 790,000 ng/bee; 7,180,000 ng/g bw, respectively), their

maximum residue levels were negligible in aspect of ecotoxicology (Tomlin,

2009). For carbaryl (acute oral LD50 230 ng/bee; 2,100 ng/g bw), only one pollen

sample collected on May 3 in Odae slightly exceeded LD50 value (2,114 ng/g

bw, 100.7% of LD50) (Food and Agriculture Organization). For this value, the

Dixon’s Q test was conducted and it was significant outlier (p <0.05).

Chlorantraniliprole, difenoconazole, diflubenzuron, fluazinam, indoxacarb,

terflubenzuron, and thiophanate-methyl showed higher maximum

concentrations (1,300-7,048 ng/g) in pollen. Except for indoxacarb, these

pesticides are non-toxic to bee (acute oral LD50 >100,000 ng/bee; >909,000

ng/g bw) (Tomlin, 2009). Indoxacarb is highly toxic (acute oral LD50 260 ng/bee;

2,400 ng/g bw), and the maximum residue in pollen was 2.5 times higher than

LD50 (Tomlin, 2009). Residue ranges in honey samples were not hazardous to

bee, whereas fluvalinate in one honey sample was exhibited a higher residue

level (966.7 ng/g) than its MRL (50 ng/g) (European Commission, 2018). These

values exceeding LD50 of indoxacarb or MRL of fluvalinate are turned out to

be significant outliers (p <0.05).

166

Table 26. Positive detection frequency for bee, pollen, and honey samples in Giran

No. Compound name Total (%)

n = 87

Bee1) Pollen Honey Registered for apple Total

(%) n = 48

Geum- gok

Muk- gye

Odae Total (%)

n = 24

Geum- gok

Muk- gye

Odae Total (%)

n = 15

Geum- gok

Muk- gye

Odae

1 Abamectin B1a 2 (2.3) - - - - 2 (8.3) 1 1 - - - - - yes 2 Acetamiprid 35 (40.2) 2 (4.2) - 2 - 19 (79.2) 7 8 4 14 (93.3) 4 5 5 yes 3 Acrinathrin 3 (3.4) - - - - 3 (12.5) 2 1 - - - - - yes 4 Atrazine 6 (6.9) - - - - 6 (25.0) 2 2 2 - - - - no2) 5 Bifenazate 17 (19.5) 8 (16.7) 1 4 3 9 (37.5) 2 2 5 - - - - yes 6 Carbaryl 37 (42.5) 14 (29.2) 4 6 4 22 (91.7) 6 8 8 1 (6.7) - - 1 yes 7 Carbendazim 2 (2.3) - - - - - - - - 2 (13.3) - 2 - yes 8 Carbofuran 8 (9.2) 2 (4.2) 1 - 1 6 (25.0) 1 4 1 - - - - no 9 Chlorantraniliprole 26 (29.9) 10 (20.8) 4 4 2 16 (66.7) 5 6 5 - - - - yes

10 Chlorpyrifos 20 (23.0) 13 (27.1) 1 5 7 7 (29.2) 2 2 3 - - - - yes 11 Cyhalothrin-lambda 8 (9.2) 3 (6.3) - 2 1 5 (20.8) 3 2 - - - - - yes 12 Cyprodinil 17 (19.5) 14 (29.2) 5 4 5 3 (12.5) 2 1 - - - - - yes 13 Deltamethrin 1 (1.1) - - - - 1 (4.2) - - 1 - - - - yes 14 Difenoconazole 11 (12.6) 9 (18.8) 5 2 2 2 (8.3) 1 - 1 - - - - yes 15 Diflubenzuron 30 (34.5) 22 (45.8) 7 6 9 8 (33.3) 4 2 2 - - - - yes 16 Diphenylamine 12 (13.8) 4 (8.3) 1 2 1 8 (33.3) 2 4 2 - - - - no3) 17 Emamectin B1a 5 (5.7) - - - - 5 (20.8) 1 3 1 - - - - yes 18 Emamectin B1b 2 (2.3) - - - - 2 (8.3) 1 1 - - - - - yes 19 Ethiofencarb 1 (1.1) - - - - 1 (4.2) - 1 - - - - - no2) 20 Etofenprox 38 (43.7) 24 (50.0) 7 9 8 14 (58.3) 4 6 4 - - - - yes 21 Fenazaquin 1 (1.1) 1 (2.1) 1 - - - - - - - - - - yes 22 Fenvalerate 1 (1.1) 1 (2.1) 1 - - - - - - - - - - yes 23 Flonicamid 11 (12.6) - - - - 7 (29.2) 3 2 2 4 (26.7) 3 - 1 yes 24 Fluacrypyrim 1 (1.1) - - - - 1 (4.2) - 1 - - - - - no4) 25 Fluazinam 14 (16.1) 5 (10.4) 3 2 - 9 (37.5) 3 2 4 - - - - yes 26 Flubendiamide 27 (31.0) 10 (20.8) 4 3 3 17 (70.8) 5 7 5 - - - - yes 27 Flufenoxuron 11 (12.6) 2 (4.2) - 2 - 9 (37.5) 1 4 4 - - - - yes 28 Fluquinconazole 17 (19.5) 9 (18.8) 3 3 3 8 (33.3) 3 2 3 - - - - yes

167



n = 87

Bee1) Pollen Honey Registered for apple Total

(%) n = 48

Geum- gok

Muk- gye

Odae Total (%)

n = 24

Geum- gok

Muk- gye

Odae Total (%)

n = 15

Geum- gok

Muk- gye

Odae

29 Fluvalinate 49 (56.3) 37 (77.1) 14 8 15 8 (33.3) 3 4 1 4 (26.7) - 2 2 no5) 30 Hexythiazox 11 (12.6) - - - - 11 (45.8) 3 4 4 - - - - yes 31 Indoxacarb 6 (6.9) 3 (6.3) 3 - - 3 (12.5) 2 1 - - - - - yes 32 Kresoxim-methyl 12 (13.8) 1 (2.1) - - 1 11 (45.8) 4 4 3 - - - - yes 33 Mepronil 1 (1.1) - - - - 1 (4.2) - 1 - - - - - no 34 Methomyl 23 (26.4) 7 (14.6) 1 5 1 16 (66.7) 5 5 6 - - - - no4) 35 Methoxyfenozide 21 (24.1) 4 (8.3) 1 2 1 17 (70.8) 5 7 5 - - - - yes 36 Novaluron 30 (34.5) 24 (50.0) 5 8 11 6 (25.0) 1 2 3 - - - - yes 37 Picoxystrobin 17 (19.5) 9 (18.8) - 7 2 8 (33.3) 2 3 3 - - - - yes 38 Pyraclostrobin 5 (5.7) - - - - 5 (20.8) 2 1 2 - - - - yes 39 Pyrimethanil 5 (5.7) - - - - 5 (20.8) 3 - 2 - - - - yes 40 Spirodiclofen 4 (4.6) - - - - 4 (16.7) - 1 3 - - - - yes 41 Spiromesifen 35 (40.2) 20 (41.7) 7 7 6 15 (62.5) 5 6 4 - - - - yes 42 Sulfoxaflor 7 (8.0) 2 (4.2) 2 - - 4 (16.7) - 3 1 1 (6.7) - 1 - yes 43 Teflubenzuron 12 (13.8) 4 (8.3) 4 - - 8 (33.3) 4 3 1 - - - - yes 44 Thiodicarb 13 (14.9) 4 (8.3) - 4 - 9 (37.5) 2 3 4 - - - - yes 45 Thiophanate-methyl 30 (34.5) 10 (20.8) 1 6 3 20 (83.3) 6 8 6 - - - - yes 46 Trifloxystrobin 5 (5.7) - - - - 5 (20.8) 2 3 - - - - - yes

1)Dead and healthy imago, and larva.

2)Not included in Pesticide Registration Status of the Republic of Korea.

3)Post-harvest deterioration inhibitor for apple or naturally producted compound in some crops.

4)Banned or suspended from sales in 2011 (methomyl) and in 2013 (fluacrypyrim).

5)Pre-treated in colony before monitoring to coltrol mites.

168

Fig. 28. Distribution of the numbers of detection frequencies for fluvalinate,

etofenprox, carbaryl, acetamiprid, and spiromesifen, which ranked first to

fifth among the pesticide multiresidues by the detection frequency

170

Table 27. Distribution of median values and residue ranges for pesticide multiresidues in Giran

No. Pesticides Honey bee Pollen ng/g

Honey ng/g Dead imago

ng/g bw Healty imago

ng/g bw Larva ng/g

Median Residue range





1 Abamectin B1a <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-86.8 <LOQ <LOQ 2 Acetamiprid <LOQ <LOQ-42.7 <LOQ <LOQ <LOQ <LOQ 5.2 <LOQ-103.6 5.5 <LOQ-21.8 3 Acrinathrin <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-22.5 <LOQ <LOQ 4 Atrazine <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-5.7 <LOQ <LOQ 5 Bifenazate <LOQ <LOQ-184.9 <LOQ <LOQ-33.7 <LOQ <LOQ <LOQ <LOQ-493.1 <LOQ <LOQ 6 Carbaryl 8.3 <LOQ-1960.9 <LOQ <LOQ <LOQ <LOQ 7.2 <LOQ-2114 <LOQ <LOQ-1.2 7 Carbendazim <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-5.7 8 Carbofuran <LOQ <LOQ-11.7 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-26.8 <LOQ <LOQ 9 Chlorantraniliprole <LOQ <LOQ-42.9 <LOQ <LOQ <LOQ <LOQ-11.2 6.7 <LOQ-2414 <LOQ <LOQ

10 Chlorpyrifos <LOQ <LOQ-91.3 <LOQ <LOQ-40.0 <LOQ <LOQ <LOQ <LOQ-748.4 <LOQ <LOQ 11 Cyhalothrin-lambda <LOQ <LOQ-112.6 <LOQ <LOQ-48.3 <LOQ <LOQ <LOQ <LOQ-202.8 <LOQ <LOQ 12 Cyprodinil 11.7 <LOQ-166.4 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-729.4 <LOQ <LOQ 13 Deltamethrin <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-25.1 <LOQ <LOQ 14 Difenoconazole <LOQ <LOQ-81.3 <LOQ <LOQ-39.6 <LOQ <LOQ <LOQ <LOQ-1821 <LOQ <LOQ 15 Diflubenzuron <LOQ <LOQ-554.2 25.6 10.4-68.6 <LOQ <LOQ-30.8 <LOQ <LOQ-1335 <LOQ <LOQ 16 Diphenylamine <LOQ <LOQ-27.8 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-130.9 <LOQ <LOQ 17 Emamectin B1a <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-30.6 <LOQ <LOQ 18 Emamectin B1b <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-33.3 <LOQ <LOQ 19 Ethiofencarb <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-5.0 <LOQ <LOQ 20 Etofenprox <LOQ <LOQ-234.6 6.4 <LOQ-15.2 <LOQ <LOQ-2.5 5.7 <LOQ-133.6 <LOQ <LOQ 21 Fenazaquin <LOQ <LOQ-11.6 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ 22 Fenvalerate <LOQ <LOQ-11.0 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ 23 Flonicamid <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-68.7 <LOQ <LOQ-12.8

171



Honey ng/g Dead imago

ng/g bw Healty imago

ng/g bw Larva ng/g bw






24 Fluacrypyrim <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-1.6 <LOQ <LOQ 25 Fluazinam <LOQ <LOQ <LOQ <LOQ-102.9 <LOQ <LOQ <LOQ <LOQ-7048 <LOQ <LOQ 26 Flubendiamide <LOQ <LOQ-75.4 <LOQ <LOQ <LOQ <LOQ 2.9 <LOQ-126.8 <LOQ <LOQ 27 Flufenoxuron <LOQ <LOQ-19.2 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-199.8 <LOQ <LOQ 28 Fluquinconazole <LOQ <LOQ-130.2 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-326.6 <LOQ <LOQ 29 Fluvalinate 42.4 <LOQ-302.0 26.0 <LOQ-103.3 47.2 <LOQ-179.5 <LOQ <LOQ-874.2 <LOQ <LOQ-966.7 30 Hexythiazox <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-43.0 <LOQ <LOQ 31 Indoxacarb <LOQ <LOQ <LOQ <LOQ-177.3 <LOQ <LOQ <LOQ <LOQ-5987 <LOQ <LOQ 32 Kresoxim-methyl <LOQ <LOQ-574.9 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-355.3 <LOQ <LOQ 33 Mepronil <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-30.1 <LOQ <LOQ 34 Methomyl <LOQ <LOQ-42.1 <LOQ <LOQ <LOQ <LOQ 23.9 <LOQ-725.3 <LOQ <LOQ 35 Methoxyfenozide <LOQ <LOQ-36.7 <LOQ <LOQ <LOQ <LOQ 5.5 <LOQ-58.5 <LOQ <LOQ 36 Novaluron <LOQ <LOQ-63.0 32.1 <LOQ-62.4 <LOQ <LOQ-10.3 <LOQ <LOQ-712.6 <LOQ <LOQ 37 Picoxystrobin <LOQ <LOQ-67.1 <LOQ <LOQ-162.2 <LOQ <LOQ <LOQ <LOQ-110.1 <LOQ <LOQ 38 Pyraclostrobin <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-371.2 <LOQ <LOQ 39 Pyrimethanil <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-480.6 <LOQ <LOQ 40 Spirodiclofen <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-31.4 <LOQ <LOQ 41 Spiromesifen <LOQ <LOQ-52.4 65.0 <LOQ-261.8 <LOQ <LOQ-38.4 14.3 <LOQ-2544 <LOQ <LOQ 42 Sulfoxaflor <LOQ <LOQ-19.1 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-14.3 <LOQ <LOQ-1.9 43 Teflubenzuron <LOQ <LOQ <LOQ <LOQ-289.4 <LOQ <LOQ-65.6 <LOQ <LOQ-1300 <LOQ <LOQ 44 Thiodicarb <LOQ <LOQ-11.4 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-5.4 <LOQ <LOQ 45 Thiophanate-methyl <LOQ <LOQ-342.9 <LOQ <LOQ-112.6 <LOQ <LOQ 25.7 <LOQ-1886 <LOQ <LOQ 46 Trifloxystrobin <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-73.2 <LOQ <LOQ

172

In Yeongyang, the area of pepper field, 36 pesticides were detected and

30 (83.3%) of them have been acceptable to treat on pepper field (Table 28)

(Korea Crop Protection Association, 2012; Korea Crop Protection Association,

2015). Because these pesticides also have been registered in other crops, some

of the pesticides might come from other sources. Fluvalinate (pyrethroid),

acephate (organophosphate), etofenprox (pyrethroid), flubendiamide (diamide),

and flonicamid (selective feeding blocker) ranked first to fifth among the

pesticides in detection frequencies in this area (Fig. 29). Etofenprox and

flubendiamide were not determined in honey samples and residue of

Flonicamid was below LOQ in bee (imago and larva) samples. The residue

ranges of these five pesticides investigated were lower than acute oral LD50s

(≥270 ng/bee; ≥2,500 ng/g bw) in all samples (Table 29) (Marletto et al., 2003;

Tomlin, 2009). The residue levels of the pesticides in the dead and healthy

imago samples were lower than 100 ng/g bw. In the larva samples, only

fluvalinate was determined and its maximum concentration (565.8 ng/g) was

higher than that in the imago samples. However, fluvalinate is not hazardous to

bee (acute oral LD50 163,000 ng/bee; 1,480,000 ng/g bw), and its maximum level

was only 0.04% of LD50 (Tomlin, 2009). In contrast to Giran, there was no

target analyte in pollen sample above 500 ng/g. Spirodiclofen (acute oral LD50

>196,000 ng/bee; >1,780,000 ng/g bw) and thiophanate-methyl (>100,000

ng/bee; >909,000 ng/g bw) showed the highest residue levels in pollen (365.1

and 320.1 ng/g, respectively) but negligible in aspect of ecotoxicology (Tomlin,

2009). Residue ranges in honey samples were not hazardous to bee and lower

than those of MRLs (20-1,000 ng/g) (European Commission, 2018).

173

In conclusion, residue levels of pesticide multiresidues which were

detected at higher concentrations in honey bee, pollen, and honey samples were

not lethal to honey bee. Although there were a few cases exceeding acute oral

LD50 of pesticides, these concentrations turned out to be significant outliers (p

<0.05). The other pesticides which were positively detected at minor levels but

not evaluated in this study may possess lethal toxicity. There are no ecotoxicity

data available for these pesticides to our knowledge. Therefore, further

systemic investigation and research are required to carry out comprehensive

risk assessment to honey bee.

174

Table 28. Positive detection frequency for bee, pollen, and honey samples in Yeongyang


n = 52

Bee1) Pollen Honey Registered for pepper Total

(%) n = 32

Sanun Dae-cheon

Total (%)

n = 10

Sanun Dae-cheon

Total (%)

n = 10

Sanun Dae-cheon

1 Abamectin B1a 1 (1.9) - - - 1 (10.0) 1 - - - - yes 2 Acephate 17 (32.7) 3 (9.4) - 3 7 (70.0) 4 3 7 (70.0) 4 3 yes 3 Acetamiprid 13 (25.0) - - - 8 (80.0) 3 5 5 (50.0) 5 - yes 4 Boscalid 1 (1.9) - - - 1 (10.0) - 1 - - - yes 5 Carbendazim 7 (13.5) - - - - - - 7 (70.0) 4 3 yes 6 Carbofuran 2 (3.8) - - - 2 (20.0) 1 1 - - - no 7 Chlorantraniliprole 7 (13.5) 1 (3.1) - 1 6 (60.0) 2 4 - - - yes 8 Chlorpyrifos 1 (1.9) - - - 1 (10.0) 1 - - - - yes 9 Cyhalothrin-lambda 3 (5.8) 3 (9.4) 1 2 - - - - - - yes 10 Difenoconazole 1 (1.9) 1 (3.1) - 1 - - - - - - yes 11 Diflubenzuron 2 (3.8) 2 (6.3) 1 1 - - - - - - yes 12 Dimethomorph 5 (9.6) 1 (3.1) - 1 4 (40.0) 2 2 - - - yes 13 Diphenylamine 8 (15.4) 8 (25.0) 4 4 - - - - - - no2) 14 Emamectin B1a 1 (1.9) - - - 1 (10.0) 1 - - - - yes 15 Etofenprox 16 (30.8) 9 (28.1) 6 3 7 (70.0) 4 3 - - - yes 16 Ferimzone 1 (1.9) - - - 1 (10.0) - 1 - - - no 17 Flonicamid 14 (26.9) - - - 6 (60.0) 5 1 8 (80.0) 5 3 yes 18 Fluacrypyrim 1 (1.9) - - - 1 (10.0) 1 - - - - no 19 Fluazinam 6 (11.5) 1 (3.1) - 1 5 (50.0) 1 4 - - - yes 20 Flubendiamide 15 (28.8) 7 (21.9) 5 2 8 (80.0) 4 4 - - - yes 21 Flufenoxuron 5 (9.6) 2 (6.3) - 2 3 (30.0) 2 1 - - - yes 22 Fluvalinate 30 (57.7) 26 (81.3) 11 15 3 (30.0) 1 2 1 (10.0) 1 - no3)

175



n = 52

Bee1) Pollen Honey Registered for pepper Total

(%) n = 32

Sanun Dae-cheon

Total (%)

n = 10

Sanun Dae-cheon

Total (%)

n = 10

Sanun Dae-cheon

23 Metalaxyl 6 (11.5) 3 (9.4) 2 1 1 (10.0) 1 - 2 (20.0) 2 - yes 24 Methomyl 5 (9.6) 1 (3.1) 1 - 4 (40.0) 2 2 - - - no4) 25 Methoxyfenozide 2 (3.8) 1 (3.1) - 1 1 (10.0) - 1 - - - yes 26 Metrafenone 2 (3.8) - - - 2 (20.0) - 2 - - - yes 27 Novaluron 11 (21.2) 10 (31.3) 5 5 1 (10.0) - 1 - - - yes 28 Picoxystrobin 1 (1.9) - - - 1 (10.0) - 1 - - - yes 29 Pyraclostrobin 8 (15.4) 1 (3.1) - 1 7 (70.0) 2 5 - - - yes 30 Spirodiclofen 2 (3.8) 1 (3.1) - 1 1 (10.0) 1 - - - - yes 31 Spiromesifen 1 (1.9) 1 (3.1) - 1 - - - - - - yes 32 Sulfoxaflor 12 (23.1) 1 (3.1) 1 - 4 (40.0) 3 1 7 (70.0) 5 2 yes 33 Teflubenzuron 2 (3.8) - - - 2 (20.0) - 2 - - - yes 34 Thiodicarb 1 (1.9) - - - 1 (10.0) 1 - - - - yes 35 Thiophanate-methyl 8 (15.4) 2 (6.3) - 2 6 (60.0) 3 3 - - - yes 36 Trifloxystrobin 6 (11.5) - - - 6 (60.0) 2 4 - - - yes

1)Dead and healthy imago, and larva.

2)Post-harvest deterioration inhibitor for apple or naturally producted compound in some crops.

3)Pre-treated in colony before monitoring to coltrol mites.

4)Banned in 2011.

176

Fig. 29. Distribution of the numbers of detection frequencies for fluvalinate,

etofenprox, acephate, etofenprox, flubendiamide, and flonicamid, which

ranked first to fifth among the pesticide multiresidues by the detection

frequency

178

Table 29. Distribution of median values and residue ranges for pesticide multiresidues in Yeongyang


Honey ng/g

Dead imago ng/g bw

Healty imago ng/g bw

Larva ng/g






1 Abamectin B1a <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-8.5 <LOQ <LOQ

2 Acephate <LOQ <LOQ-55.9 <LOQ <LOQ <LOQ <LOQ 3.7 <LOQ-16.1 4.4 <LOQ-14.0

3 Acetamiprid <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ 6.8 <LOQ-78.6 2.0 3.4-12.7

4 Boscalid <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-13.0 <LOQ <LOQ

5 Carbendazim <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ 1.3 <LOQ-4.8

6 Carbofuran <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-1.4 <LOQ <LOQ

7 Chlorantraniliprole <LOQ <LOQ-14.9 <LOQ <LOQ <LOQ <LOQ 2.0 <LOQ-11.5 <LOQ <LOQ

8 Chlorpyrifos <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-10.7 <LOQ <LOQ

9 Cyhalothrin-lambda <LOQ <LOQ-19.9 <LOQ <LOQ-25.6 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ

10 Difenoconazole <LOQ <LOQ-20.9 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ

11 Diflubenzuron <LOQ <LOQ-37.9 <LOQ <LOQ-9.2 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ

12 Dimethomorph <LOQ <LOQ-14.4 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-62.7 <LOQ <LOQ

13 Diphenylamine 34.0 <LOQ-96.7 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ

14 Emamectin B1a <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-1.1 <LOQ <LOQ

15 Etofenprox 2.3 <LOQ-12.3 <LOQ <LOQ-9.5 <LOQ <LOQ 2.5 <LOQ-29.7 <LOQ <LOQ

16 Ferimzone <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-5.9 <LOQ <LOQ

17 Flonicamid <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ 2.3 <LOQ-57.0 8.6 <LOQ-39.3

18 Fluacrypyrim <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-67.5 <LOQ <LOQ

179



Honey ng/g

Dead imago ng/g bw

Healty imago ng/g bw

Larva ng/g






19 Fluazinam <LOQ <LOQ-19.7 <LOQ <LOQ <LOQ <LOQ 1.3 <LOQ-152.8 <LOQ <LOQ

20 Flubendiamide <LOQ <LOQ-20.6 <LOQ <LOQ <LOQ <LOQ 2.7 <LOQ-71.7 <LOQ <LOQ

21 Flufenoxuron <LOQ <LOQ-54.1 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-6.9 <LOQ <LOQ

22 Fluvalinate 12.1 6.2-70.4 17.5 <LOQ-43.4 39.4 <LOQ-565.8 <LOQ <LOQ-139.4 <LOQ <LOQ-4.9

23 Metalaxyl <LOQ <LOQ-4.1 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-189.9 <LOQ <LOQ-1.3

24 Methomyl <LOQ <LOQ-9.9 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-12.2 <LOQ <LOQ

25 Methoxyfenozide <LOQ <LOQ-15.9 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-2.3 <LOQ <LOQ

26 Metrafenone <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-8.2 <LOQ <LOQ

27 Novaluron 10.8 <LOQ-55.3 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-58.2 <LOQ <LOQ

28 Picoxystrobin <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-5.6 <LOQ <LOQ

29 Pyraclostrobin <LOQ <LOQ-15.3 <LOQ <LOQ <LOQ <LOQ 2.8 <LOQ-58.4 <LOQ <LOQ

30 Spirodiclofen <LOQ <LOQ <LOQ <LOQ-24.4 <LOQ <LOQ <LOQ <LOQ-365.1 <LOQ <LOQ

31 Spiromesifen <LOQ <LOQ-52.1 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ

32 Sulfoxaflor <LOQ <LOQ-4.8 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-123.5 5.0 <LOQ-14.3

33 Teflubenzuron <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-112.9 <LOQ <LOQ

34 Thiodicarb <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ-1.5 <LOQ <LOQ

35 Thiophanate-methyl <LOQ <LOQ-72.5 <LOQ <LOQ <LOQ <LOQ 5.1 <LOQ-320.1 <LOQ <LOQ

36 Trifloxystrobin <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ 4.6 <LOQ-22.1 <LOQ <LOQ

180

Conclusions

Three neonicotinoids (clothianidin, imdacloprid, and thiamethoxam) and 391

pesticide multiresidues in honey bee (dead imago, healthy imago, and larva),

pollen, and honey were analyzed using the LC-MS/MS or GC-MS/MS. The

scheduled MRM modes of LC- and GC-MS/MS were employed for high-

throughput analysis and QuEChERS with a citrate buffer were used to sample

treatments. For the three neonicotinoids, their LOQs in LC-MS/MS were 1 ng/g,

respectively, thus the analytical method sufficiently determined pesticide

residues below acute oral LD50. The recovery range of the neonicotinoids was

74.4-116.2% (RSD 0.4-17.1%) at 1, 5, and 10 ng/g in bee, pollen, and honey

samples, which indicate that the established method exhibited excellent

accuracy and precision.

In the field monitoring near the area of apple orchard and pepper field, three

neonicotinids and 52 of 391 pesticides were determined in a total of 139

apiculture samples. The residue levels of the neonicotinoids and mainly

detected multiresidues were not lethal to honey bee except for a few compounds.

The compounds exceeding acute oral LD50 were turned to be outliers in aspect

of statistics. To explain these outliers, an exposure model should be established

based on vast regional statistics of multiresidues. Some minor pesticides cannot

evaluated in an aspect of ecotoxicology due to no ecotoxicity data available to

our knowledge. Synergistic toxicity between pesticides is also an important

factor to be considered, but data related are also insufficient. Therefore, further

systemic investigation and research including are should be conducted to

conclude comprehensive risk assessment to honey bee. Nevertheless, this study

181

is valuable itself as the first attempt to determine controversial neonicotinoids

as well as pesticide multiresidues in honey bees and their products in the

Republic of Korea. This field monitoring result can be an important information

to improve knowledge of honey bee exposure and on how pesticides move from

agricultural fields to the environment.

183

Chapter III

Multiresidue Analysis for 384 Pesticides in Pepper,

Orange, Brown Rice, and Soybean Using Florisil

Solid-phase Extraction and GC-MS/MS

184

Introduction

Introduction of positive list system

Pesticide residues in agricultural products are regulated by governmental

authorities to secure the health of populations. The Republic of Korea is one of

the countries with lower self-sufficiency rate of food, thus a more endeavor is

required to investigate various kinds of imported agricultural products. The

current food safety system for pesticide residues in the Republic of Korea is

based on the Negative List System (NLS), in which the pesticide residue in

Maximum Residue Levels (MRLs) list is regulated (Ministry of Food and Drug

Safety); however, the Positive List System (PLS) will be enforced from 2019

onward instead of the NLS.

The PLS is one of the food safety systems in which strict safety

management of food from all pesticides with/without MRL is performed (Table

30). In the PLS, the levels of pesticide residue should be below 0.01 mg/kg

when an MRL has not been established in corresponding crops and food

(Iwasaki et al., 2007). The PLS has been successfully implemented for many

years in various countries including the United States, Australia, Canada, Hong

Kong, Japan, Taiwan, and the European Union (EU). In Korea, the PLS has

been implemented for tropical and subtropical fruits, nuts, and seeds since

December 31, 2016, and will be applied to all agricultural products starting on

January 1, 2019 (Ministry of Food and Drug Safety).

185

Table 30. The current pesticide regulation in crops and PLS to be introduced in the Republic of Korea (Ministry of Food and

Drug Safety)

MRL established for the pesticide

Before introduction for the PLS After introduction of the PLS

Established Apply the MRL that is set Apply the MRL that is set (Same as before enforcement of the PLS)

Not established 1. Apply the CODEX standards for the particular agricultural product (excluding crop groupings). 2. Apply the lowest of the standards set for similar agricultural products. 3. Apply the lowest limit set for the pesticide concerned.

Apply the uniform level of 0.01 mg/kg

186

Tandem mass spectrometry for pesticide multiresidue analysis

Introduction of the PLS requires high-level analytical techniques to determine

a trace concentration of hundreds of pesticides in various food matrices.

Tandem mass spectrometry has been widely used as an analytical tool to

simultaneously detect and quantity pesticide multiresidues in various matrix

origins (Soler and Picó, 2007; LeDoux, 2011). In general, most of tandem mass

spectrometers utilized in quantitative analysis of multiresidue is a triple

quadrupole mass spectrometer (QqQ), which consist of two quadrupole

analyzers (Q) to purify molecular mass ion by m/z and one collision quadrupole

(q) aligned between the quadrupole analyzers. Tandem mass spectrometry also

can be applicable to a combination of quadrupole (Q) and high-resolution mass

(HRMS) analyzer such as time-of-flight (TOF) or orbitrap (Cheng et al., 2017;

Goon et al., 2018).

The multiple reaction monitoring (MRM) of QqQ is one of the recent

mass spectrometric techniques that is an improved concept of a selected ion

monitoring (SIM) in single mass spectrometry. The two analyzers (Q) are

conducted as SIM, whereas the second analyzer (Q3) select one of the

fragments (product ions) of a precursor ion that is purified from the first

analyzer (Q1). Precursor ion is fragmented by collision induced dissociation

(CID) during passing through the q (Q2). The MRM is superior to SIM in an

aspect of sensitivity and selectivity. Wong et al. established multiresidue

analysis for 168 pesticides in dried ginseng powders using GC-MS and GC-

MS/MS and outstanding specificity and sensitivity of target analytes were

observed in tandem mass spectrometry than in single mass spectrometry (Wong

et al., 2010).

187

Many works of literatures have reported the QqQ coupled with liquid

chromatography (LC) or gas chromatography (GC) (Vázquez et al., 2016; Han

et al., 2017; Wu, 2017). The LC and GC served as an compound purification

and separation technique to prevent contamination of mass spectrometer by

sample matrices and to distribute massive spectrometric data by retention time.

Supercritical fluid chromatography (SFC) and ion chromatography (IC) are also

available with mass spectrometry for specific types of pesticides (Adams et al.,

2017; Cutillas et al., 2018).

Table 31 showed recently established analytical methods for pesticide

multiresidues by the tandem mass spectrometry in various type of agricultural

product matrices during three-year period (2016-2018).

188

Table 31. Review of the tandem mass spectrometry for pesticide multiresidues in agricultural products during three-year

publication (2016-2018)

No. Matrix Instrument Number of

analytes

Reference

1 Tomato, orange, and leek SFC1)-MS/MS 164 (Cutillas et al., 2018)

2 Sweet pepper LC-MS/MS 21 (da Costa Morais et al., 2018)

3 Spices LC-MS/Orbitrap

199 (Goon et al., 2018)

4 Kiwifruit LC-MS/MS 49 (Kim et al., 2018)

5 Brown rice, orange, and spinach LC-MS/MS 310 (Lee et al., 2018b)

6 Teas GC-MS/MS 128 (Li et al., 2018a)

7 Mango GC-MS/MS and

LC-MS/MS

113 (Li et al., 2018b)

8 Lettuce LC-MS/MS 16 (Ribeiro Begnini Konatu and Sales Fontes Jardim, 2018)

9 Cardamom GC-MS/MS 243 (Shabeer et al., 2018)

10 Flour and grape IC2)-MS/MS 12 (Adams et al., 2017)

189



analytes

Reference

11 Apple, pear, tomato, cucumber, and cabbage

GC-MS/TOF3) 15 (Cheng et al., 2017)

12 Rice, wheat, and corn GC-MS/MS 124 (Han et al., 2017)

13 Vegetable oils GC-MS/MS 255 (He et al., 2017)

14 Cardamom LC-MS/MS 154 (Jadhav et al., 2017)

15 Wheat, rye, oat LC-MS/MS 23 (Kaczyński and Łozowicka, 2017)

16 Pear LC-MS/MS 170 (Kemmerich et al., 2018)

17 Brown rice, spinach, orange, and potato GC-MS/MS 360 (Lee et al., 2017)

18 Tomato, sweet pepper LC-MS/MS 21 (Martins et al., 2017)

19 Rice GC-MS/MS 31 (Mondal et al., 2017)

20 Apple, citrus fruit, peanut, wheat, tea, and spinach

LC-MS/MS 23 (Qin et al., 2017)

21 Lettuce LC-MS/MS 16 (Ribeiro Begnini Konatu et al., 2017)

22 Tomato, leek LC-MS/MS 41 (Robles-Molina et al., 2017)

190



analytes

Reference

23 Currants, raspberries, cherries, strawberries, blackberries, cauliflowers,

and broccoli

LC-MS/MS 60 (Stachniuk et al., 2017)

24 Crop plants LC-MS/MS 72 (Viera et al., 2017)

25 Oolong tea GC-MS/MS 89 (Wu, 2017)

26 Rice LC-MS/MS 20 (Cabrera et al., 2016)

27 Olive oil LC-MS/MS 165 (Dias et al., 2016)

28 Wheat LC-MS/MS 42 (Friedrich et al., 2016)

29 Rice and wheat flour GC-MS/MS 100 (Grande-Martínez et al., 2016)

30 Cowpea GC-MS/MS 171 (Han et al., 2016)

31 Green tea GC-MS/MS 101 (Hou et al., 2016)

32 Olive oil, olives, and avocado LC-MS/MS 67 (López-Blanco et al., 2016)

33 Lettuce and orange GC-MS/MS and

LC-MS/MS

175 (Lozano et al., 2016)

191



analytes

Reference

34 Sugar beet and beet molasses GC-MS/MS and

LC-MS/MS

>400 (Lozowicka et al., 2016)

35 Spinach LC-MS/MS 44 (Qin et al., 2016a)

36 Apple, citrus fruit, peanut, wheat, tea, and spinach

LC-MS/MS 25 (Qin et al., 2016b)

37 Vegetable oils GC-MS/MS 213 (Vázquez et al., 2016)

38 Tomato GC-MS/MS >140 (Walorczyk et al., 2016) 1)Supercritical fluid chromatography

2)Ion chromatography

3)Time-of-flight

192

Solid-phase extraction for pesticide purification

With the tandem mass spectrometry, sample treatment is an important factor to

conduct pesticide analysis. Well-established sample preparation exert the best

extraction efficiency with rugged results, thus trace levels of detection limit of

target analytes can be obtained in the same instrument condition. The ideal

methodology to preserve the integrity of the target pesticide is sample

extraction without purification step. When using this treatment, however, it is

difficult to distinguish between unremoved matrices and multi-analytes no

matter how a mass spectrometer is superior. The interferences also cause a

severe matrix effects, so that target analytes may not be detected at all. The

cleaning and maintanance cycle of mass spectrometric parts such as an ion

source also become shorter due to numerous matrices. A proper purification

procedure removing matrices from pesticides is, therefore, required.

Solid-phase extraction (SPE) is one of the cleanup techniques first

introduced since the mid-1970s (Sabik et al., 2000). General SPE is performed

by passing sample extract or liquid sample itself through a solid sorbent in a

glass or polypropylene cartridge. It is advantageous in that it can be purified

strongly with a small amount of solvent, thus, alternative to liquid-liquid

extraction or column chromatography. Since a SPE cartridge was commercially

available in 1978, numerous types of sorbent in difference cartridge sizes allow

analysts to have a chance to analyze pesticides with various chemical properties

(Picó et al., 2007).

The SPE is also applicable in pesticide multiresidue analysis. It is a

challenge to establish optimum washing/elution conditions for multi-analyte

characteristics. Many of literatures overcome this issue in various ways (Table

193

32). Yang et al. (2011) tried dual SPE with two different types of SPE and

obtained excellent recoveries for 88 pesticides in raspberries, strawberries,

blueberries, and grapes (Yang et al., 2011). SPE can be combined with the

“Quick, Easy, Cheap, Effective, Rugged, and Safe” (QuEChERS) procedure

that is a strong extraction and partitioning methodology (Anastassiades et al.,

2003). Chen et al. (2011) and Hou et al. (2016) established analytical methods

using QuEChERS for the sample extraction and partitioning and then SPE for

purification (Chen et al., 2011; Hou et al., 2016).

Multiclass Pesticide Multiresidue Method (No. 2)

The Multiclass Pesticide Multiresidue Method (No. 2) of the Korea Food Code

is an official analytical method for analysis of pesticide multiresidues in crops

(Ministry of Food and Drug Safety). The method has powerful sample

treatment procedures using an amino-propyl (NH2) for LC-amenable pesticides

and a forisil for GC-amenable pesticides. Among the sorbents, florisil consists

of a magnesium silicate with a high polarity. Florisil has strong purification

characteristics for pesticides from fatty samples due to its ability to

preferentially retain some lipids (Żwir-Ferenc and Biziuk, 2006). Polar matrices

such as chlorophylls triglycerides and phytosterols, common substances of

fruits and vegetables are also easily removed from the sample extract by

associating with the surface of florisil (Torres et al., 1996). Thus, the Multiclass

Pesticide Multiresidue Method (No. 2) is a golden standard to determine

pesticides in various kinds of agricultural products and food.

194

Table 32. Representative analytical methods for pesticide multiresidues including solid-phase extraction (SPE) cleanup

procedures

No. Matrix SPE type Instrument Number of

analytes

Reference

1 Green tea GCB/PSA GC-MS/MS 101 (Hou et al., 2016) 2 Tea Sep-Pak

Carbon NH2 LC-MS/MS 65 (Chen et al., 2011)

3 Raspberries, strawberries, blueberries, and grapes

Envi-Carb & NH2-LC coupled

GC-MS 88 (Yang et al., 2011)

4 Dried ginseng powders C8 and GCB/PSA

GC-MS and GC-MS/MS

168 (Wong et al., 2010)

5 Honey, fruit juice, and wine Envi-Carb & Sep-Pak NH2

coupled

GC-MS and

LC-MS/MS

450 (Pang et al., 2006)

6 Juice Silica Bondesil-C18

GC-MS 50 (Albero et al., 2005)

7 White grapes Sep-Pak Silica

LC-DAD 14 (Rial Otero et al., 2003)

8 Egg GCB NH2

Florisil

GC-ECD and FPD

36 (Schenck and Donoghue, 2000)

195


In this study, a simultaneous multiresidue analytical method was developed

using GC-MS/MS. A scheduled MRM mode of GC-MS/MS was employed for

an effective throughput of target pesticides. Four representative crops popular

in Korea were selected as matrices; pepper (high pigment and chlorophyll),

orange (high acidic compounds), brown rice (high starch), and soybean (high

protein and fat). The official Multiclass Pesticide Multiresidue Method (No. 2)

of the Korea Food Code was scaled down and validated with 384 pesticides.

For removing fat in soybean sample, liquid-liquid partitioning method using n-

hexane/acetonitrile was also investigated for comparing to non-partitioning

method. The evaluated analytical method is applicable for the PLS as well as

the rapid and sensitive monitoring of pesticide multiresidues in pepper, orange,

brown rice, and soybean and their related agriculture products.

196



Each pesticide standard (analytical grade) or stock solution (10, 100, 500 or

1,000 mg/L) was purchased from ChemService (West Chester, PA), Wako Pure

Chemical Industries (Osaka, Japan), Dr. Ehrenstorfer (Augsburg, Germany),

Sigma-Aldrich (St. Louis, MO), Tokyo Chemical Industry (Tokyo, Japan),

AccuStandard (New Haven, CT) and ULTRA Scientific (North Kingstown, RI),

or thankfully obtained from Laboratory of Environmental Chemistry of

Kyungpook National University (the Republic of Korea), and Ministry of Food

and Drug Safety (Republic of Korea). HPLC grades of acetonitrile, acetone,

methanol were sourced from Fisher Scientific (Seoul, Republic of Korea).

Sodium chloride (NaCl, 99.0%) was obtained from Samchun (Gyeonggi-do,

the Republic of Korea). Diethylene glycol (≥99.0%) was purchased from

Sigma-Aldrich. Strata-FL-PR florisil cartridge (500 mg/6 mL) was obtained

from Phenomenex (Torrance, CA).

Preparation of matrix-matched standard

Each analytical standard was dissolved in acetonitrile, acetone, or methanol in

accordance with their solubility to make 1,000 mg/L of stock solutions. A

portion of each stock solution from standards or commercial products was then

mixed and diluted with acetonitrile so that the concentration of four groups of

intermediate mixed stock solutions became 10 mg/L in each mixed standard

solution. The aliquots of intermediates were again mixed to make a final mixed

standard solution at 2.5 mg/L. This solution was subjected to further serial

197

dilution using acetonitrile to prepare working solutions at 1, 0.5, 0.25, 0.1, 0.05,

0.025, and 0.01 mg/L. These solutions were finally mixed with matrix solutions

from blank samples at a ratio of 1:4 (v/v) to prepare matrix-matched standards

at 0.2, 0.1, 0.05, 0.02, 0.01, 0.005, and 0.002 mg/L.

Instrumental conditions of GC-MS/MS

Pesticide multiresidues were separated by a Shimadzu GC-2010 plus furnished

with an AOC-20i autosampler (Kyoto, Japan) and analyzed by a Shimadzu

GCMS-TQ8040 triple quadrupole mass spectrometer (Kyoto, Japan). The GC

conditions followed our previous work (Lee et al., 2017). Briefly, the inlet

temperature was 280 °C and the injection volume was 2 μL. A Topaz glass liner

(3.5-mm) with wool (Restek, Bellefonte, PA) was installed within the inlet and

splitless mode was used during sample injection. The capillary column was

Rxi-5Sil MS (30 m × 0.25 mm i.d., 0.25 μm df, Restek, Bellefonte, PA). The

oven temperature program was initialized with 70 °C (held for 2 min), ramped

to 160 °C at 15 °C/min, then increased to 260 °C at 5 °C/min, and finally

ramped to 300 °C at 15 °C/min (held for 8 min). The total program time was

38.7 min. Helium (≥99.999%) was used as carrier gas and flow rate (constant)

was 1.0 mL/min.

For the MS/MS conditions, the electron ionization (EI) mode was

selected for ionization and electron voltage was 70 eV. The ion source and

transfer line temperature were 230 and 280 °C, respectively. The collision

inductive dissociation (CID) was assisted with argon (≥99.999%) gas. The

detector voltage was 1.4 kV (constant). The data processing was conducted

using LabSolutions (GCMS solution, version 4.30).

198

Multiple reaction monitoring (MRM) profile optimization

Each pesticide standard solution was injected into GC-MS/MS, respectively,

and full scan spectrum of each target was obtained (m/z range; 50-500). From

the spectrum, one or two of a fragment(s) or molecular ion were selected as a

precursor ion(s) considering intensity and selectivity. Each precursor ion was

subjected to product scan using CID with various collision energy (CE), and

two optimum product ions were selected as a quantifier ion and a qualifier ion,

respectively.

Sample preparation of pepper, orange, brown rice, and soybean

Before sample preparation, pepper, orange, brown rice, and soybean were

homogenized respectively with dry ice using a blender. Preparation of each crop

was conducted using modified Multiclass Pesticide Multiresidue Method (No.

2) from the Korea Food Code (Ministry of Food and Drug Safety). Ten gram of

an aliquot was transferred into a 50-mL centrifuge tube. For brown rice and

soybean, 6 mL water was added to the tube to let the entire sample soak the

water sufficiently. The aliquot was extracted with 20 mL of acetonitrile. The

tube was shaken for 2 min at 1,200 rpm using Geno Grinder (1600 MiniG SPEX

Sample Prep, Metuchen, NJ), and then the sample was subjected to suction

filtration under vacuum. The extract was treated with 3 g of NaCl and then

centrifuged for 5 min at 3,500 rpm using Combi 408 (Hanil Science Industrial

Co., Ltd., Korea). The supernatant (8 mL) was treated with 0.2 mL of 2%

diethylene glycol in acetone and evaporated with a rotary evaporator (40 °C)

and reconstructed in 4 mL of 20% acetone in n-hexane (v/v).

199

For SPE cleanup step, a florisil cartridge (500 mg) was conditioned with

5 mL n-hexane and 5 mL of 20% acetone in n-hexane. Reconstructed extract (4

mL) was loaded and the loaded cartridge was eluted without washing step. The

cartridge was eluted again with 5 mL of 20% acetone in n-hexane. The eluted

extracts were combined and then evaporated to dryness under a stream of

nitrogen gas. The sample was reconstructed in 2 mL acetonitrile, and 0.8 mL of

the extract was matrix-matched with 0.2 mL of acetonitrile. Finally, the aliquot

(2 μL) was injected into GC-MS/MS for target analytes separation and analysis.

The sample was equivalent to 1.6 g per 1 mL in the final extract.

Defatting procedure in soybean using n-hexane/acetonitrile partitioning

Partitioning step with n-hexane and acetonitrile was conducted before the SPE

cleanup, and its extraction and cleanup efficiency were compared to the method

without the partitioning procedure. Briefly, 10 g of soybean was subjected to

extraction, filtration, NaCl partitioning, and evaporation, as described above.

The extract was then dissolved in 30 mL of n-hexane saturated with acetonitrile

and partitioned with 30 mL of acetonitrile saturated with n-hexane (twice). The

lower layers from each partition were collected, and treated with 0.2 mL of 2%

diethylene glycol in acetone, and then evaporated with rotary evaporator (40 °C)

for the next cleanup step, as described above.

Method validation

The method limit of quantitation (MLOQ) was calculated from the instrumental

limit of quantitation (ILOQ), injection volume, and sample equivalent in the

final extract as following equation:

200

MLOQ (𝑚𝑚𝑛𝑛 ∙ 𝑘𝑘𝑛𝑛−1) =ILOQ (𝑎𝑎𝑛𝑛)

injection volume (𝜇𝜇𝜇𝜇) ×1

sample equivalent (𝑛𝑛 ∙ 𝑚𝑚𝜇𝜇−1)

ILOQ was determined by the signal to noise ratio (S/N) method (De

Bièvre et al., 2005). Matrix-matched standards in pepper, orange, brown rice,

and soybean samples were injected into GC-MS/MS, respectively, and the

lowest amount of chromatogram which satisfied the S/N value ≥10 for each

pesticide was selected as ILOQ. Instrumental repeatability for each pesticide

was verified with RSD of peak area by injecting and analyzing matrix-matched

standard seven times. The linearity of calibration was determined using matrix-

matched standards in each crop sample. Instrumental linear range was

investigated from 0.002 mg/L to 0.2 mg/L (equivalent to 0.00125-0.125 mg/kg

of method linear range). A weighting regression factor (1/x) was employed to

minimize calculation errors at low concentrations. The correlation coefficient

(r2) of calibration was calculated for each target analyte. Recovery of each

pesticide was determined at 0.01 and 0.05 mg/kg. To conduct the recovery tests,

10 g of each blank sample was treated with mixed standard solutions,

respectively, and prepared as described above (n = 3). The recovery samples

were compared with matrix-matched standards to verify extraction efficiencies.

The matrix effect was verified to compare a slope of calibration form a matrix-

matched standard and from a solvent-based standard. Matrix effect values (%)

for each pesticide were calculated as following equation:



201


MRM optimization and selection of pesticides to be validated

A total of 397 pesticides was selected to be studied. Using standard solutions

of each pesticide, MRM profiles were successfully established. Retention times

were also verified under GC conditions. It is noticeable that deltamethrin and

tralomethrin had the same MRM conditions (precursor ion > product ion; 253

> 172 for quantifier and 253 > 174 for qualifier) with the same retention times

(32.01 and 32.27 min), thus they could not be distinguished. It is possible that

tralomethrin was debrominated at the inlet of GC and converted to deltamethrin

(Woudneh and Oros, 2006). According to Pesticide MRLs in Food published in

the Republic of Korea , MRLs of tralomethrin follow that of deltamethrin

(Ministry of Food and Drug Safety, 2017). Therefore, deltamethrin and

tralomethrin shared the same validation results.

Using the matrix-matched standards of pepper, orange, brown rice, and

soybean at 0.2 mg/L MRM chromatograms for 397 pesticides were verified.

Anilazine, daimuron, and pyrazosulfuron-ethyl were not detected at all and

captafol and captan had a poor sensitivity in all crop samples. Therefore, these

five pesticides were discarded from the final method validation. Recovery

samples were also investigated and three compounds (naled, oxydemeton-

methyl, and schradan) were not recovered in all samples. Benzyladenine,

nicotine, tecloftalam, triflusulfuron-methyl, and trinexapac-ethyl showed the

low recovery values below 30% in all crops. Thus, these eight pesticides were

also excluded from the list of target analytes according to the guidance of

202

SANTE/11813/2017 (European Commission, 2017). Finally, the remaining of

384 analytes from 397 pesticides was investigated in further validation steps.

Characteristics of 384 pesticides

Among the pesticides to be established, 57.6% was listed in Pesticide MRLs in

Food, accounting for 45.9% of 466 MRL entries in 2017 (Ministry of Food and

Drug Safety, 2017). For pesticide activities, 163 (42.4%) of 384 pesticides was

insecticide (covering acaricide and nematicide), 86 (22.4%) was fungicide

(covering bactericide), 108 (28.1%) was herbicide, and 6 (1.6%) was plant

growth regulator. Inter-activity pesticides that exhibit activities in two or more

different groups were accounted for 13 (3.4%) of the total. In addition, the

pesticide chemical groups were divided into eight, which were carbamate (25

compounds), organochlorine (66), organophosphate (87), pyrethroid (19),

triazine (16), triazole (22), urea (7), and others/unclassified (142). Among the

384 pesticides, 13 compounds were major metabolites of alachlor (alachlor-2-

hydroxy, [2',6'-diethyl-N-2-hydroxy(methoxymethyl)acetanilide]), amitraz

(BTS 27919 [2,4-dimethylformanilide]), chlordane (oxychlordane),

chlorothalonil (pentachlorobenzonitrile), DDT (o,p'-DDD, p,p'-DDD, o,p'-

DDE, and p,p'-DDE), endosulfan (endosulfan-sulfate), heptachlor (heptachlor-

epoxide), and quintozene (pentachloroaniline, pentachlorobenzene, and

pentachlorothioanisole). These metabolites have been found in various

environmental matrices or crop residues (Dejonckheere et al., 1976; el-

Nabarawy and Carey, 1988; Kross et al., 1992; Eitzer et al., 2001;

Golfinopoulos et al., 2003; Gong et al., 2004; Ziaei and Amini, 2013).

203

Individual characteristics of information of the 384 pesticides is given in Table

S3.

Comparison of the preparation procedures with/without n-hexane/

acetonitrile partitioning

The optimum soybean preparation method was selected between the procedures

with/without n-hexane/acetonitrile partitioning. To verify extraction efficiency,

the recovery test at 0.05 mg/kg was conducted in both procedures. The

percentages of 203 pesticides satisfying the criteria of recovery 70-120% (RSD

≤20%) were 74.5% for partitioning and 84.4% for non-partitioning (European

Commission, 2017). Some of the pesticides with higher log P were remained

in the hexane layer during partitioning. To verify the cleanup efficiency, scan

chromatograms of control soybean samples were compared (Fig. 30). At tR

values of 5.0-10.0 min and 26.5-27.0 min, more impurities were detected when

the non-partitioning procedure was applied than when the partitioned procedure

was applied, whereas larger peaks were observed when the partitioning

procedure was applied at a tR value of 31.0 min or greater. There were no

significant differences in the cleanup between the procedures. From the results,

the preparation procedures without n-hexane/acetonitrile partitioning (non-

partitioning) was considered as the most appropriate soybean treatment method

for GC–MS/MS analysis.

204

Fig. 30. Scan chromatograms (m/z 50-500) for control soybean samples of

(a) partitioned and (b) non-partitioned procedures

206

Method limit of quantitation (MLOQ)

Matrix-matched standard mixtures at 0.004, 0.01, 0.02, 0.04, 0.1, 0.2, and 0.4

ng (equivalent to 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, and 0.2 mg/L matrix-

matched standard solutions) in pepper, orange, brown rice, and soybean were

injected into the GC-MS/MS and the lowest amount satisfying S/N ≥10 on the

chromatogram for each pesticide was selected as ILOQ. The MLOQ was

derived from the ILOQ, injection volume (2 μL), and sample equivalent (1.6

g/mL) in the final extract. If ILOQ is 0.004 ng, MLOQ can be calculated using

equation as described above.

MLOQ (𝑚𝑚𝑛𝑛 ∙ 𝑘𝑘𝑛𝑛−1) =ILOQ (𝑎𝑎𝑛𝑛)

injection volume (𝜇𝜇𝜇𝜇) ×1

sample equivalent (𝑛𝑛 ∙ 𝑚𝑚𝜇𝜇−1)

MLOQ (𝑚𝑚𝑛𝑛 ∙ 𝑘𝑘𝑛𝑛−1) = 0.004 (𝑎𝑎𝑛𝑛) 2 (𝜇𝜇𝜇𝜇)⁄ × 1 1.6 (𝑛𝑛 ∙ 𝑚𝑚𝜇𝜇−1)⁄

= 0.0013 (𝑚𝑚𝑛𝑛 ∙ 𝑘𝑘𝑛𝑛−1)

Among the 384 pesticides, 380 (99.0%), 381 (99.2%), 365 (95.1%), and

382 (99.5%) were MLOQ below 0.01 mg/kg in pepper, orange, brown rice, and

soybean, respectively, showing excellent sensitivity in the analytical method

(Table 33). Only 2 (0.5%) to 3 (0.8%) compounds showed MLOQ >0.01 mg/kg

in some crops. Carbosulfan and propargite were not detectable in pepper. The

percentage was slightly lower in brown rice than other crops because 16

compounds were not determined at all investigation ranges. Among them, eight

compounds were azines (ametryn, bupirimate, cyanazine, cyprazine,

dimethametryn, prometryn, simetryn, and terbutryn) and four were azoles

(azaconazole, cyproconazole, flusilazole, myclobutanil). They exhibited

severely broaden peaks only in brown rice samples. Nevertheless, the overall

207

percentages satisfying MLOQ below 0.01 mg/kg were similar or greater than

those in our previous study using QuEChERS method with GC-MS/MS (Lee

et al., 2017). Thus, this analytical method can determine pesticide multiresidues

in pepper, orange, brown rice, soybean, and related crop with sufficient

sensitivity.

208

Table 33. Distribution of MLOQs for 384 pesticides in pepper, orange, brown

rice, and soybean

MLOQ

Crop No. of analytes

Pepper Orange Brown rice Soybean

≤ 0.01 380 (99.0%) 381 (99.2%) 365 (95.1%) 382 (99.5%)

> 0.01 2 (0.5%) 3 (0.8%) 3 (0.8%) 2 (0.5%)

N.D.1) 2 (0.5%) 0 (0.0%) 16 (4.2%) 0 (0.0%)

Sum 384 (100%) 384 (100%) 384 (100%) 384 (100%)

1)Not determined.

209

Instrumental repeatability

The instrumental repeatability is a parameter to ensure the integrity of

instrumental performance for target compounds (Zhao and Lee, 2001; Lee et

al., 2018a). Repeatability test was conducted by consecutively injecting matrix-

matched standard solutions at 0.01 and 0.05 mg/L (n = 7) and verified RSD of

area for each pesticide. Average RSDs of the target pesticides were 2.3-4.3% at

0.01 mg/L and 1.3-2.6% at 0.05 mg/L, showing better repeatability at higher

concentration (0.05 mg/L) in all crops. For the 384 pesticides in pepper, orange,

brown rice, and soybean samples, RSDs for 372 (96.9%), 377 (98.2%), 362

(94.3%), and 378 (98.4%) at 0.01 mg/L and 375 (97.7%), 383 (99.7%), 364

(94.8%), and 384 (100.0%) at 0.05 mg/L were below than 10% (Table 34),

respectively. This indicates that GC-MS/MS exerted excellent performance for

the most pesticides during sample injection and analysis. RSDs for 3 (0.8%) to

5 (1.3%) at 0.01 mg/L and 0 to 4 (1.0%) at 0.05 mg/L of total pesticides fell

within 10 to 20% in all crops. These analytes still showed acceptable

repeatability considering that RSD criterion for recovery is ≤20% according to

the SANTE guidance document (European Commission, 2017).

Fenfuram, folpet, methoxychlor, and p,p’-DDT in pepper, and

chlorothalonil in orange obtained RSDs more than 20% at 0.01 or 0.05 mg/L.

These compounds tended to decrease in area as the number of injections

increased (Fig. 31), thus require auxiliary approaches such as internal standard

calibration (Nguyen et al., 2008) to correct quantitation. Except for the

compounds, target pesticides in all crops presented the excellent instrumental

repeatability on established GC-MS/MS conditions.

210

Linearity of calibration

The linearity of matrix-matched calibration was determined with the linear

range from LOQ to 0.2 mg/L. Among the 384 target analytes, 377 (98.2%), 375

(97.7%), 364 (94.8%), and 379 (98.7%) showed correlation coefficient (r2)

≥0.990 in pepper, orange, brown rice, soybean, respectively (Table 35). Only

r2 of 2 (0.5%) to 3 (0.8%) pesticides were within 0.980-0.990 and r2 of 2 (0.5%)

to 6 (1.6%) were <0.980. Therefore, most of the pesticides obtained precise

quantitative analysis properties.

211

Table 34. Summary of instrumental repeatability to show distribution of RSD of area for 384 pesticides in pepper, orange, brown

rice, and soybean (n = 7)

Crop Pepper No. of analytes

Orange No. of analytes

Brown rice No. of analytes

Soybean No. of analytes

RSD (area)

0.01 mg/L

0.05 mg/L

0.01 mg/L

0.05 mg/L

0.01 mg/L

0.05 mg/L

0.01 mg/L

0.05 mg/L

≤10% 372 (96.9%)

375 (97.7%)

377 (98.2%)

383 (99.7%)

362 (94.3%)

364 (94.8%)

378 (98.4%)

384 (100.0%)

10-20% 5 (1.3%)

4 (1.0%)

3 (0.8%)

0 (0.0%)

4 (1.0%)

4 (1.0%)

4 (1.0%)

0 (0.0%)

>20% 2 (0.5%)

3 (0.8%)

0 (0.0%)

1 (0.3%)

0 (0.0%)

0 (0.0%)

0 (0.0%)

0 (0.0%)

N.D. 5 (1.3%)

2 (0.5%)

4 (1.0%)

0 (0.0%)

18 (4.7%)

16 (4.2%)

2 (0.5%)

0 (0.0%)

Sum 384 (100%)

384 (100%)

384 (100%)

384 (100%)

384 (100%)

384 (100%)

384 (100%)

384 (100%)

N.D.; Not determined.

212

Fig. 31. Relative peak area (100 at 1st injection) of DDT-p,p', fenfuram,

folpet, methoxychlor (pepper), and chlorothalonil (orange) at 50 ng/mL to

show peak decreases as the number of injections increases

213

30

40

50

60

70

80

90

100

1st 2nd 3rd 4th 5th 6th 7th

Rel

ativ

e Are

a

Injection no.

DDT-p,p' (Pepper) Fenfuram (Pepper) Folpet (Pepper)Methoxychlor (Pepper) Chlorothalonil (Orange)

214


pepper, orange, brown rice, and soybean

r2

Crop No. of pesticides

Pepper Orange Brown rice Soybean

≥0.990 377 (98.2%) 375 (97.7%) 364 (94.8%) 379 (98.7%)

0.980-0.990 3 (0.8%) 3 (0.8%) 2 (0.5%) 3 (0.8%)

<0.980 2 (0.5%) 6 (1.6%) 2 (0.5%) 2 (0.5%)

N.D. 2 (0.5%) 0 (0.0%) 16 (4.2%) 0 (0.0%)

Sum 384 (0.0%) 384 (0.0%) 384 (0.0%) 384 (0.0%)


215

Recovery

For the efficient sample treatment, sample and extraction solvent of Multiclass

Pesticide Multiresidue Method (No. 2) of the Korea Food Code (Ministry of

Food and Drug Safety) were reduced from 50 to 10 g and 100 to 20 mL

acetonitrile. During the evaporation at 40 °C, diethylene glycol in acetone, a

keeper solution (Gunther et al., 1962), was added in each sample to prevent

some volatile pesticides from evaporation with the solvent. To maintain the

optimal and rugged performance of the florisil SPE, the sorbent was prevented

from drying out during the conditioning, sample-loading, and elution steps, as

Mutavdžić et al. (2006)’s instruction (Mutavdžić et al., 2006).

The recovery test was conducted at 0.01 and 0.05 mg/kg. As a result,

323 (84.1%) to 354 (92.2%) pesticides at 0.01 mg/kg and 324 (84.4%) to 354

(92.2%) at 0.05 mg/kg satisfied the acceptable recovery criteria of 70-120%

with RSD ≤20% (Table 36) in pepper, orange, brown rice, and soybean

(European Commission, 2017). The number of pesticides satisfying the criteria

at 0.05 mg/kg was slightly higher than that at 0.01 mg/kg in orange, brown rice,

and soybean. The recovery of pepper indicated the same percentage (92.2%) at

both treated levels and showed the largest number of pesticides among the crops.

These compounds can be detectable with highly reliable trueness and precision

properties.

216

Table 36. Distribution of recoveries for 384 pesticides in pepper, orange, brown rice, and soybean

Recovery range

% RSD

%

Pepper No. of analytes (%)

Orange No. of analytes (%)

Brown rice No. of analytes (%)

Soybean No. of analytes (%)

0.01 mg/kg

0.05 mg/kg

0.01 mg/kg

0.05 mg/kg

0.01 mg/kg

0.05 mg/kg

0.01 mg/kg

0.05 mg/kg

<30 ≤0 2 (0.5) 2 (0.5) 6 (1.6) 8 (2.1) 2 (0.5) 4 (1.0) 3 (0.8) 5 (1.3)

30-70 ≤20 23 (6.0) 26 (6.8) 34 (8.9) 35 (9.1) 24 (6.3) 20 (5.2) 51 (13.3) 54 (14.1)

>20 0 (0.0) 0 (0.0) 3 (0.8) 0 (0.0) 0 (0.0) 0 (0.0) 3 (0.8) 1 (0.3)

70-120 ≤20 354 (92.2) 354 (92.2) 334 (87.0) 339 (88.3) 336 (87.5) 343 (89.3) 323 (84.1) 324 (84.4)

>20 1 (0.3) 0 (0.0) 2 (0.5) 0 (0.0) 1 (0.3) 0 (0.0) 0 (0.0) 0 (0.0)

120-140 ≤20 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)

>20 0 (0.0) 0 (0.0) 0 (0.0) 1 (0.3) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)

>140 ≤0 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (0.3) 0 (0.0) 0 (0.0)

N.D. 4 (1.0) 2 (0.5) 5 (1.3) 1 (0.3) 21 (5.5) 16 (4.2) 4 (1.0) 0 (0.0)

Sum 384 (100) 384 (100) 384 (100) 384 (100) 384 (100) 384 (100) 384 (100) 384 (100)


217

The percentage of pesticides out of the criteria (recovery 30-70% or

120-140%) but still within RSD ≤20% were 5.2 to 14.1% in all crops. The

recoveries of these compounds are consistent (European Commission, 2017),

so still acceptable for a screening purpose. The remaining of pesticides (0.5-

2.1%; recovery <30% or 140%, or RSD >20%) requires alternative ways to

correct recoveries such as employment of internal standard calibration (Nguyen

et al., 2008) or isotope dilution (Bravo et al., 2002; Focant et al., 2004).

For more detailed information, recovery results were divided by

pesticide activities. Fig. 32 showed the percentages of pesticides which fell

recovery 70-120% (RSD ≤20%) by six groups of activities in pepper, orange,

brown rice, and soybean. As a result, 75-100% of target pesticides satisfied the

criteria in insecticide, fungicide, herbicide, inter-activity, and others. Plant

growth regulator also showed the excellent percentages (83%) in pepper,

orange, and brown rice, whereas half of the pesticides were not included in the

criteria in soybean. These plant growth regulators (2-(1-naphthyl)acetamide,

2,6-diisopropylnaphthalene, and ethychlozate) can be acceptable for screening

purpose due to the constant recovery results (RSD ≤5.0%).

218

Fig. 32. Percentages of pesticides satisfying recovery 70-120% (RSD ≤20%)

classified by activity groups

220

When the recovery results were classified as chemical group covering

the recovery 70-120% (RSD ≤20%), six groups (carbamate, organophosphate,

pyrethroid, triazole, urea, and others/unclassified) showed relatively excellent

percentage ranges from 72% to 100% at treated levels of 0.01 and 0.05 mg/kg.

(Fig. 33) Organochlorine pesticides also indicated high percentages from target

pesticides in pepper, orange, and brown rice (82-88%), but showed the low

percentage (61%) in soybean at both treated level. The organochlorines out of

the recovery criteria only in soybean was non-polar pesticides such as aldrin,

DDE (both of o,p’- and p,p’-), heptachlor, cis-nonachlor, pentachloroaniline,

pentachlorothioanisole, quintozene, and tecnazene. The issues were not caused

by cleanup steps but by extraction steps. The fat content of soybean is 20% (20

g/100 g total weight) of the total (USDA). It is possible that some of the non-

polar pesticides were strongly adsorbed on by non-polar fat, thus there

pesticides were not extracted sufficiently with acetonitrile. This problem can be

solved by separating the pesticides from the fat by freezing. In the established

method using freezing extraction and florisil dispersive-SPE (dSPE), 95

pesticides covering the problematic pesticides in our study obtained acceptable

recoveries in soybean oil (Nguyen et al., 2010). The percentages of triazine

pesticides were accounted for 56-63% in orange and brown rice. Because some

of triazines in brown rice were rejected to be determined by severe peak

broadening, thus this contributed to the low percentages. Further investigation

is needed for problematic triazines in orange because there was no problem with

validation parameters such as LOQ, linearity of calibration, and repeatability.

These pesticides showed excellent recoveries with the same crop in Lee et al.

221

(2017)’s report using acidified extraction solvent (0.1% formic acid in

acetonitrile) and PSA dSPE (Lee et al., 2017).

In conclusion, although the sample amount and extraction solvent

volumes were reduced from the original method, this analytical method is still

valid for determining most of the pesticide in representative crops and

applicable for 93.8-99.0% of the total pesticides for screening purpose. For a

few pesticides with recovery problems in some crops, alternative methods will

be useful.

222

Fig. 33. Percentages of pesticides satisfying recovery 70-120% (RSD ≤20%)

classified by chemical groups at (a) 0.01 mg/kg and (b) 0.05 mg/kg

224

Matrix effect

The matrix effect when analyzing pesticides using GC is a common

phenomenon (Hajšlová and Zrostlı́ková, 2003). Erney et al. (1993) did first

discuss matrix-induced signal enhancement of organophosphate pesticides on

GC (Erney et al., 1993). One of the causes inducing matrix effects in GC is a

masking effect in which matrices and target analytes competitively interact with

active sites of a liner during injection (Hajšlová and Zrostlı́ková, 2003). Many

works of literature have reported the signal enhancement by crop matrices on

GC-MS or GC-MS/MS (Schenck and Lehotay, 2000; Lehotay et al., 2010; He

et al., 2015; Lee et al., 2017).

The averages of matrix effects for target pesticides were 14.4%, 17.0%,

25.2%, and 5.9% in pepper, orange, brown rice, and soybean, respectively.

Signals of most of the pesticides were enhanced by matrices in all crops. Brown

rice and orange matrices gave the greatest and second greatest influence

between the crops, respectively, but showed lower matrix effects than those in

other multiresidue works (He et al., 2015; Lee et al., 2017). Matrix effects

distribution classified as six group ranges (Fig. 34) indicated that most of the

pesticides were included in Group 4 (0% to 20% of matrix effect range) or

Group 5 (20% to 50%), exhibiting soft to medium enhancement effects.

Pesticides in the soft group (61.0%, 69.3%, 37.8%, and 94.0% of the total in

each four crop) are not required to matrix-matching because matrix effect is

negligible (Rajski et al., 2013; He et al., 2015). The medium (Group 2 and 4)

and strong (Group 1 and 6) group should employ matrix-matching to correct

quantitation on GC-MS/MS.

225

Conclusions

A simultaneous multiresidue method of 384 pesticides using solid-phase

extraction (SPE) was validated in pepper, orange, brown rice, and soybean by

gas chromatography-tandem mass spectrometry (GC-MS/MS). The MRM on

GC-MS/MS was optimized with electron ionization (EI) mode. Among the total

pesticides, 95.1-99.5% satisfied the MLOQ below than 0.01 mg/kg in all crops.

This indicates that most of the pesticides are applicable in PLS in which a

residue level should be under 0.01 mg/kg for the pesticide without MRL lists

of each crop. The numbers of pesticides satisfying recovery range of 70-120%

with relative standard deviation (RSD) ≤20% were 84.1-92.2% at 0.01 mg/kg

and 84.4-92.2% at 0.05 mg/kg showing excellent trueness and precision in the

analytical method. Furthermore, 93.8-99.0% of 384 pesticides (recovery 30-

140% and RSD ≤20%) was applicable for multiresidue screening purpose. The

average matrix effect values (%) for four crops were 5.9% to 25.2%, indicating

that crop matrices caused a signal enhancement on GC-MS/MS. This

established methods can be sufficiently applied for the rapid and sensitive

monitoring of pesticide multiresidues in pepper, orange, brown rice, soybean,

and their related crops.

226

Fig. 34. Distribution of matrix effects for 384 pesticides in pepper, orange,

brown rice, and soybean. Group 3 and 4 are included in soft matrix effect,

Group 2 and 5 in medium effect, and Group 1 and 6 in strong effect

228

Supplementary Information

Table S1. The retention times (tR), monoisotopic masses, quasi-molecular ion

types, and MRM transitions of LC-MS/MS for the multiresidual pesticides

No. Compound name tR (min)

Mono Isotopic

mass

Quasi-molecular

ion



1 2,4-D 4.56 220 [M-H]- 219 > 161 (12) 219 > 125 (27)

2 Abamectin B1a 9.39 872 [M+NH4]+ 890 > 305 (-28) 890 > 567 (-15)

3 Acephate 2.82 183 [M+H]+ 184 > 143 (-9) 184 > 49 (-21)

4 Acetamiprid 3.27 222 [M+H]+ 223 > 126 (-19) 223 > 56 (-16)

5 Acibenzolar-S-methyl 5.58 210 [M+H]+ 211 > 135 (-31) 211 > 91 (-20)

6 Aldicarb 3.70 190 [M+NH4]+ 208 > 116 (-7) 208 > 89 (-16)

7 Allidochlor 3.82 173 [M+H]+ 174 > 98 (-13) 174 > 41 (-22)

8 Ametoctradin 7.37 275 [M+H]+ 276 > 176 (-37) 276 > 149 (-38)

9 Ametryn 5.05 227 [M+H]+ 228 > 186 (-15) 228 > 68 (-40)

10 Amisulbrom 7.67 465 [M+H]+ 466 > 227 (-22) 466 > 226 (-14)

11 Amitraz 8.91 293 [M+H]+ 294 > 163 (-16) 294 > 122 (-31)

12 Asulam 2.91 230 [M+H]+ 231 > 156 (-10) 231 > 92 (-23)

13 Atrazine 4.77 215 [M+H]+ 216 > 174 (-16) 216 > 68 (-35)

14 Azaconazole 4.83 299 [M+H]+ 300 > 159 (-29) 300 > 231 (-17)

15 Azamethiphos 3.87 324 [M+H]+ 325 > 182 (-18) 325 > 112 (-40)

16 Azimsulfuron 4.66 424 [M+H]+ 425 > 182 (-19) 425 > 139 (-39)

17 Azinphos-ethyl 6.12 345 [M+H]+ 346 > 132 (-15) 346 > 77 (-37)

18 Bendiocarb 4.02 223 [M+H]+ 224 > 167 (-9) 224 > 109 (-18)

19 Benfuracarb 7.83 410 [M+H]+ 411 > 195 (-25) 411 > 190 (-12)

20 Bensulfuron-methyl 5.00 410 [M+H]+ 411 > 149 (-20) 411 > 182 (-21)

21 Bensulide 6.48 397 [M+H]+ 398 > 158 (-24) 398 > 314 (-11)

22 Bentazone 3.81 240 [M-H]- 239 > 132 (25) 239 > 133 (24)

23 Benthiavalicarb-isopropyl 5.76 381 [M+H]+ 382 > 180 (-29) 382 > 116 (-22)

24 Benzobicyclon 5.63 446 [M+H]+ 447 > 257 (-25) 447 > 229 (-38)

25 Benzoximate 7.21 363 [M+H]+ 364 > 199 (-12) 364 > 105 (-23)

26 Bifenazate 5.91 300 [M+H]+ 301 > 198 (-10) 301 > 170 (-20)

27 Boscalid 5.47 342 [M+H]+ 343 > 139 (-19) 343 > 112 (-40)

28 Bromacil 4.10 260 [M+H]+ 261 > 205 (-14) 261 > 188 (-28)

29 Bromoxynil 4.52 275 [M-H]- 274 > 79 (27) 274 > 167 (32)

30 Bupirimate 6.23 316 [M+H]+ 317 > 166 (-26) 317 > 108 (-27)

31 Butafenacil 6.00 474 [M+NH4]+ 492 > 331 (-22) 492 > 180 (-45)

32 Butocarboxim 3.64 190 [M+NH4]+ 208 > 75 (-8) 208 > 47 (-30)

33 Cafenstrole 5.81 350 [M+H]+ 351 > 100 (-11) 351 > 72 (-29)

34 Carbaryl 4.22 201 [M+H]+ 202 > 145 (-10) 202 > 127 (-27)

35 Carbendazim 3.14 191 [M+H]+ 192 > 160 (-17) 192 > 132 (-29)

229


Mono Isotopic

mass

Quasi-molecular

ion



36 Carbofuran 4.05 221 [M+H]+ 222 > 165 (-13) 222 > 123 (-22)

37 Carboxin 4.25 235 [M+H]+ 236 > 142 (-16) 236 > 87 (-25)

38 Carfentrazone-ethyl 6.63 411 [M+NH4]+ 429 > 412 (-12) 429 > 346 (-23)

39 Chlorantraniliprole 4.95 481 [M+H]+ 482 > 283 (-15) 482 > 450 (-17)

40 Chlorfenvinphos 6.89 358 [M+H]+ 359 > 155 (-13) 359 > 99 (-28)

41 Chloridazon 3.37 221 [M+H]+ 222 > 77 (-36) 222 > 104 (-23)

42 Chlorimuron-ethyl 5.58 414 [M+H]+ 415 > 186 (-19) 415 > 185 (-24)

43 Chlorotoluron 4.54 212 [M+H]+ 213 > 72 (-23) 213 > 46 (-15)

44 Chromafenozide 6.05 394 [M+H]+ 395 > 175 (-16) 395 > 339 (-8)

45 Cinmethylin 8.11 274 [M+H]+ 275 > 105 (-21) 275 > 153 (-7)

46 Clofentezine 7.22 302 [M+H]+ 303 > 138 (-14) 303 > 102 (-35)

47 Clomeprop 7.94 323 [M+H]+ 324 > 120 (-21) 324 > 203 (-16)

48 Cyanazine 3.84 240 [M+H]+ 241 > 214 (-17) 241 > 104 (-30)

49 Cyazofamid 6.20 324 [M+H]+ 325 > 108 (-14) 325 > 261 (-10)

50 Cycloate 7.43 215 [M+H]+ 216 > 54 (-38) 216 > 133 (-18)

51 Cycloprothrin 8.80 481 [M+NH4]+ 499 > 257 (-15) 499 > 181 (-36)

52 Cyclosulfamuron 5.95 421 [M+H]+ 422 > 261 (-17) 422 > 217 (-27)

53 Cymoxanil 3.46 198 [M+H]+ 199 > 128 (-7) 199 > 111 (-17)

54 Cyromazine 2.71 166 [M+H]+ 167 > 85 (-20) 167 > 60 (-21)

55 Deltamethrin 9.05 505 [M+NH4]+ 523 > 506 (-12) 523 > 281 (-18)

56 Demeton-S-Methyl 4.11 230 [M+H]+ 231 > 89 (-8) 231 > 61 (-31)

57 Diafenthiuron 8.83 384 [M+H]+ 385 > 329 (-20) 385 > 278 (-34)

58 Dicrotophos 3.09 237 [M+H]+ 238 > 112 (-12) 238 > 193 (-9)

59 Diethofencarb 5.29 267 [M+H]+ 268 > 226 (-9) 268 > 124 (-32)

60 Diflubenzuron 6.35 310 [M+H]+ 311 > 158 (-14) 311 > 141 (-30)

61 Diflufenican 7.55 394 [M+H]+ 395 > 266 (-25) 395 > 246 (-35)

62 Dimethachlor 4.98 255 [M+H]+ 256 > 223 (-14) 256 > 148 (-27)

63 Dimethoate 3.33 229 [M+H]+ 230 > 199 (-9) 230 > 125 (-20)

64 Dimethomorph 5.61 387 [M+H]+ 388 > 301 (-21) 388 > 165 (-32)

65 Dimethylvinphos 5.87 332 [M+H]+ 333 > 127 (-13) 333 > 207 (-19)

66 Diuron 4.85 232 [M+H]+ 233 > 72 (-21) 233 > 160 (-25)

67 Emamectin B1a 7.99 886 [M+H]+ 886 > 158 (-42) 886 > 82 (-55)

68 Emamectin B1b 7.70 872 [M+H]+ 872 > 158 (-36) 872 > 82 (-55)

69 Ethaboxam 4.39 320 [M+H]+ 321 > 183 (-23) 321 > 200 (-26)

70 Ethametsulfuron-methyl 4.27 410 [M+H]+ 411 > 196 (-19) 411 > 168 (-31)

71 Ethiofencarb 4.39 225 [M+H]+ 226 > 107 (-14) 226 > 77 (-43)

72 Ethoxyquin 5.20 217 [M+H]+ 218 > 148 (-22) 218 > 174 (-29)

73 Ethoxysulfuron 5.69 398 [M+H]+ 399 > 261 (-16) 399 > 218 (-25)

74 Etofenprox 9.83 376 [M+NH4]+ 394 > 177 (-14) 394 > 359 (-12)

75 Famoxadone 6.89 374 [M+NH4]+ 392 > 331 (-11) 392 > 238 (-18)

76 Fenhexamid 6.02 301 [M+H]+ 302 > 97 (-25) 302 > 55 (-40)

77 Fenobucarb 5.26 207 [M+H]+ 208 > 95 (-14) 208 > 152 (-8)

78 Fenoxaprop-P-ethyl 7.75 361 [M+H]+ 362 > 288 (-18) 362 > 121 (-28)

79 Fenoxycarb 6.47 301 [M+H]+ 302 > 88 (-21) 302 > 116 (-12)

230


Mono Isotopic

mass

Quasi-molecular

ion



80 Fenpyroximate 8.85 421 [M+H]+ 422 > 366 (-17) 422 > 138 (-32)

81 Fentrazamide 6.77 349 [M+H]+ 350 > 197 (-8) 350 > 83 (-22)

82 Ferimzone 5.24 254 [M+H]+ 255 > 91 (-33) 255 > 132 (-19)

83 Flonicamid 3.05 229 [M-H]- 228 > 81 (10) 228 > 146 (20)

84 Fluacrypyrim 7.43 426 [M+H]+ 427 > 205 (-11) 427 > 145 (-25)

85 Fluazinam 8.14 464 [M-H]- 463 > 416 (19) 463 > 398 (17)

86 Flubendiamide 6.58 682 [M+H]+ 683 > 408 (-9) 683 > 273 (-32)

87 Flufenacet 6.15 363 [M+H]+ 364 > 151 (-21) 364 > 194 (-12)

88 Flufenoxuron 8.57 488 [M+H]+ 489 > 158 (-19) 489 > 141 (-45)

89 Flumiclorac-pentyl 7.95 423 [M+NH4]+ 441 > 308 (-23) 441 > 354 (-15)

90 Flumioxazin 4.96 354 [M+H]+ 355 > 327 (-14) 355 > 299 (-27)

91 Fluopicolide 5.70 382 [M+H]+ 383 > 173 (-22) 383 > 145 (-50)

92 Fluopyram 5.99 396 [M+H]+ 397 > 173 (-28) 397 > 208 (-22)

93 Fluquinconazole 5.96 375 [M+H]+ 376 > 349 (-19) 376 > 307 (-26)

94 Flusulfamide 7.04 414 [M-H]- 413 > 171 (38) 413 > 349 (23)

95 Fluvalinate 9.36 502 [M+H]+ 503 > 181 (-29) 503 > 208 (-13)

96 Fonofos 6.88 246 [M+H]+ 247 > 109 (-20) 247 > 137 (-11)

97 Forchlorfenuron 4.75 247 [M+H]+ 248 > 129 (-15) 248 > 93 (-33)

98 Furathiocarb 7.93 382 [M+H]+ 383 > 194 (-16) 383 > 251 (-16)

99 Halosulfuron-methyl 6.01 434 [M+H]+ 435 > 182 (-20) 435 > 139 (-45)

100 Haloxyfop 6.55 361 [M+H]+ 362 > 316 (-17) 362 > 91 (-32)

101 Hexaflumuron 7.56 460 [M+H]+ 461 > 158 (-16) 461 > 141 (-41)

102 Hexazinone 4.08 252 [M+H]+ 253 > 170 (-15) 253 > 71 (-35)

103 Hexythiazox 8.38 352 [M+H]+ 353 > 228 (-16) 353 > 168 (-24)

104 Imazamox 3.37 305 [M+H]+ 306 > 261 (-22) 306 > 193 (-27)

105 Imazapic 3.44 275 [M+H]+ 276 > 231 (-21) 276 > 163 (-27)

106 Imazaquin 3.99 311 [M+H]+ 312 > 267 (-21) 312 > 199 (-29)

107 Imazethapyr 3.77 289 [M+H]+ 290 > 245 (-21) 290 > 177 (-28)

108 Imazosulfuron 5.53 412 [M+H]+ 413 > 156 (-21) 413 > 153 (-14)

109 Imibenconazole 8.07 410 [M+H]+ 411 > 125 (-29) 411 > 171 (-20)

110 Imicyafos 3.72 304 [M+H]+ 305 > 201 (-21) 305 > 235 (-17)

111 Iprovalicarb 6.06 320 [M+H]+ 321 > 119 (-15) 321 > 203 (-9)

112 Isofenphos-methyl 6.67 331 [M+H]+ 332 > 230 (-15) 332 > 121 (-33)

113 Isoprocarb 4.64 193 [M+H]+ 194 > 95 (-15) 194 > 137 (-9)

114 Isoproturon 4.76 206 [M+H]+ 207 > 72 (-16) 207 > 46 (-16)

115 Isopyrazam 7.36 359 [M+H]+ 360 > 244 (-24) 360 > 340 (-15)

116 Isoxathion 7.12 313 [M+H]+ 314 > 105 (-17) 314 > 286 (-9)

117 Lactofen 8.00 461 [M+NH4]+ 479 > 344 (-15) 479 > 223 (-35)

118 Lepimectin A3 9.27 705 [M+H]+ 706 > 153 (-17) 706 > 688 (-11)

119 Lepimectin A4 9.58 719 [M+H]+ 720 > 167 (-17) 720 > 702 (-11)

120 Linuron 5.37 248 [M+H]+ 249 > 182 (-14) 249 > 160 (-19)

121 Mandipropamid 5.51 411 [M+H]+ 412 > 328 (-15) 412 > 125 (-33)

122 Mefenpyr-diethyl 7.00 372 [M+H]+ 373 > 327 (-13) 373 > 160 (-33)

123 Mepanipyrim 6.12 223 [M+H]+ 224 > 77 (-41) 224 > 106 (-26)

231


Mono Isotopic

mass

Quasi-molecular

ion



124 Metalaxyl 4.72 279 [M+H]+ 280 > 220 (-14) 280 > 192 (-19)

125 Metamifop 7.81 440 [M+H]+ 441 > 288 (-19) 441 > 123 (-29)

126 Metazosulfuron 5.32 475 [M+H]+ 476 > 182 (-21) 476 > 156 (-21)

127 Methiocarb 5.43 225 [M+H]+ 226 > 169 (-10) 226 > 121 (-19)

128 Methomyl 3.04 162 [M+H]+ 163 > 88 (-9) 163 > 106 (-10)

129 Methoxyfenozide 5.75 368 [M+H]+ 369 > 149 (-19) 369 > 313 (-8)

130 Metolachlor 6.33 283 [M+H]+ 284 > 252 (-15) 284 > 176 (-27)

131 Metolcarb 3.86 165 [M+H]+ 166 > 109 (-11) 166 > 94 (-32)

132 Metrafenone 7.20 408 [M+H]+ 409 > 209 (-15) 409 > 227 (-19)

133 Milbemectin A3 9.29 528 [M+H-H2O]+ 511 > 95 (-32) 511 > 113 (-19)

134 Nicosulfuron 3.84 410 [M+H]+ 411 > 182 (-21) 411 > 213 (-18)

135 Novaluron 7.66 492 [M+H]+ 493 > 158 (-20) 493 > 141 (-41)

136 Omethoate 2.88 213 [M+H]+ 214 > 183 (-11) 214 > 125 (-21)

137 Oxamyl 2.95 219 [M+NH4]+ 237 > 72 (-15) 237 > 90 (-8)

138 Oxaziclomefone 7.84 375 [M+H]+ 376 > 190 (-16) 376 > 161 (-29)

139 Pebulate 7.34 203 [M+H]+ 204 > 57 (-17) 204 > 128 (-10)

140 Pencycuron 7.26 328 [M+H]+ 329 > 125 (-28) 329 > 218 (-15)

141 Penoxsulam 4.18 483 [M+H]+ 484 > 195 (-31) 484 > 194 (-40)

142 Pentoxazone 7.85 353 [M+NH4]+ 371 > 286 (-17) 371 > 354 (-9)

143 Phosmet 5.10 317 [M+H]+ 318 > 160 (-14) 318 > 77 (-53)

144 Phoxim 7.10 298 [M+H]+ 299 > 77 (-29) 299 > 129 (-10)

145 Picolinafen 8.10 376 [M+H]+ 377 > 238 (-26) 377 > 359 (-20)

146 Picoxystrobin 6.51 367 [M+H]+ 368 > 145 (-21) 368 > 205 (-10)

147 Pirimicarb 4.10 238 [M+H]+ 239 > 72 (-30) 239 > 182 (-13)

148 Promecarb 5.62 207 [M+H]+ 208 > 109 (-15) 208 > 151 (-9)

149 Propachlor 4.76 211 [M+H]+ 212 > 170 (-15) 212 > 94 (-27)

150 Propamocarb 2.89 188 [M+H]+ 189 > 102 (-20) 189 > 74 (-26)

151 Propaquizafop 8.00 443 [M+H]+ 444 > 100 (-20) 444 > 56 (-25)

152 Propazine 5.45 229 [M+H]+ 230 > 146 (-22) 230 > 188 (-16)

153 Propham 4.64 179 [M+H]+ 180 > 138 (-9) 180 > 120 (-14)

154 Propisochlor 6.82 283 [M+H]+ 284 > 224 (-10) 284 > 148 (-20)

155 Propoxur 4.02 209 [M+H]+ 210 > 111 (-13) 210 > 168 (-7)

156 Propyzamide 5.77 255 [M+H]+ 256 > 190 (-14) 256 > 173 (-21)

157 Pymetrozine 2.86 217 [M+H]+ 218 > 105 (-20) 218 > 78 (-43)

158 Pyraclostrobin 7.03 387 [M+H]+ 388 > 194 (-12) 388 > 163 (-25)

159 Pyrazolynate 7.20 438 [M+H]+ 439 > 91 (-35) 439 > 173 (-19)

160 Pyribenzoxim 8.04 609 [M+H]+ 610 > 413 (-12) 610 > 180 (-21)

161 Pyributicarb 8.29 330 [M+H]+ 331 > 181 (-16) 331 > 108 (-29)

162 Pyridate 9.52 378 [M+H]+ 379 > 207 (-18) 379 > 351 (-11)

163 Pyrifenox 6.07 294 [M+H]+ 295 > 93 (-22) 295 > 67 (-54)

164 Pyrimethanil 5.33 199 [M+H]+ 200 > 107 (-24) 200 > 82 (-26)

165 Pyrimisulfan 4.83 419 [M+H]+ 420 > 370 (-20) 420 > 255 (-28)

166 Pyriproxyfen 8.27 321 [M+H]+ 322 > 96 (-14) 322 > 78 (-51)

167 Pyroquilon 3.97 173 [M+H]+ 174 > 132 (-22) 174 > 117 (-33)

232


Mono Isotopic

mass

Quasi-molecular

ion



168 Quinalphos 6.66 298 [M+H]+ 299 > 97 (-31) 399 > 163 (-23)

169 Quinmerac 3.42 221 [M+H]+ 222 > 204 (-14) 222 > 141 (-33)

170 Quinoclamine 3.94 207 [M+H]+ 208 > 105 (-25) 208 > 77 (-39)

171 Quizalofop-ethyl 7.80 372 [M+H]+ 373 > 299 (-18) 373 > 91 (-31)

172 Rimsulfuron 4.15 431 [M+H]+ 432 > 182 (-20) 432 > 325 (-15)

173 Saflufenacil 5.06 500 [M+H]+ 501 > 198 (-44) 501 > 349 (-26)

174 Sethoxydim 8.04 327 [M+H]+ 328 > 178 (-19) 328 > 282 (-12)

175 Simazine 4.16 201 [M+H]+ 202 > 132 (-19) 202 > 124 (-18)

176 Spinosyn A 6.89 731 [M+H]+ 732 > 142 (-32) 732 > 98 (-55)

177 Spinosyn D 7.32 745 [M+H]+ 746 > 142 (-32) 746 > 98 (-55)

178 Spirodiclofen 8.84 410 [M+H]+ 411 > 71 (-21) 411 > 313 (-12)

179 Spiromesifen 8.57 370 [M+H]+ 371 > 273 (-11) 371 > 255 (-23)

180 Sulfoxaflor 3.35 277 [M+H]+ 278 > 174 (-9) 278 > 154 (-27)

181 Sulprofos 8.46 322 [M+H]+ 323 > 219 (-16) 323 > 247 (-12)

182 TCMTB 5.37 238 [M+H]+ 239 > 180 (-12) 239 > 136 (27)

183 Tebufenozide 6.50 352 [M+H]+ 353 > 133 (-19) 353 > 297 (-9)

184 Teflubenzuron 8.09 380 [M-H]- 379 > 339 (11) 379 > 359 (7)

185 Tetrachlorvinphos 6.51 366 [M+H]+ 367 > 127 (-15) 367 > 206 (-37)

186 Thenylchlor 6.13 323 [M+H]+ 324 > 127 (-14) 324 > 53 (-54)

187 Thiabendazole 3.32 201 [M+H]+ 202 > 175 (-23) 202 > 131 (-33)

188 Thiacloprid 3.41 252 [M+H]+ 253 > 126 (-20) 253 > 99 (-43)

189 Thidiazuron 3.99 220 [M+H]+ 221 > 102 (-15) 221 > 30 (-30)

190 Thifensulfuron-methyl 3.81 387 [M+H]+ 388 > 167 (-16) 388 > 205 (-26)

191 Thiodicarb 4.29 354 [M+H]+ 355 > 88 (-16) 355 > 108 (-16)

192 Thiometon 4.57 246 [M+H]+ 247 > 89 (-10) 247 > 61 (-31)

193 Thiophanate-methyl 3.90 342 [M+H]+ 343 > 151 (-20) 343 > 311 (-11)

194 Tiadinil 5.87 269 [M+H]+ 270 > 101 (-20) 270 > 103 (-20)

195 Tolfenpyrad 8.08 383 [M+H]+ 384 > 197 (-26) 384 > 154 (-43)

196 Tribenuron-methyl 4.56 395 [M+H]+ 396 > 155 (-14) 396 > 181 (-20)

197 Tribufos 9.17 314 [M+H]+ 315 > 57 (-25) 315 > 169 (-16)

198 Tricyclazole 3.58 189 [M+H]+ 190 > 163 (-20) 190 > 136 (-26)

199 Trifloxystrobin 7.52 408 [M+H]+ 409 > 186 (-19) 409 > 145 (-43)

200 Trimethacarb 4.84 193 [M+H]+ 194 > 137 (-11) 194 > 122 (-25)

201 Triticonazole 6.07 317 [M+H]+ 318 > 70 (-17) 318 > 125 (-32)

202 Uniconazole 5.95 291 [M+H]+ 292 > 70 (-24) 292 > 125 (-30)

203 Vamidothion 3.24 287 [M+H]+ 288 > 146 (-14) 288 > 58 (-40)

204 Vernolate 7.33 203 [M+H]+ 204 > 128 (-10) 204 > 43 (-19)

205 XMC 4.43 179 [M+H]+ 180 > 123 (-10) 180 > 108 (-19)

233

Table S2. The optimized GC-MS/MS parameters including retention times (tR)

and MRM transitions for each pesticide

No. Name tR (min)

Precursor ion > Product ion (CE, eV) Quantification Identification

1-1 Acrinathrin_1 28.03 181 > 152 (-30) 208 > 181 (-15) 1-2 Acrinathrin_2 28.38 181 > 152 (-30) 208 > 181 (-15) 2 Alachlor 16.57 188 > 160 (-10) 160 > 130 (-25) 3 Aldrin 18.02 263 > 193 (-30) 293 > 220 (-30) 4 Anilofos 26.62 226 > 157 (-15) 184 > 157 (-10) 5 Azinphos-methyl 28.47 132 > 77 (-15) 160 > 77 (-15) 6 Azoxystrobin 34.41 344 > 329 (-15) 388 > 300 (-15) 7 BHC-alpha 12.81 181 > 145 (-15) 219 > 183 (-10) 8 BHC-beta 13.54 181 > 145 (-15) 219 > 183 (-15) 9 BHC-delta 14.77 181 > 145 (-15) 219 > 183 (-10) 10 BHC-gamma 13.83 181 > 145 (-15) 219 > 183 (-10) 11 Bifenox 26.68 341 > 310 (-10) 343 > 312 (-10) 12 Bifenthrin 26.11 181 > 165 (-20) 181 > 166 (-15) 13 Bromobutide 16.30 119 > 91 (-10) 232 > 176 (-10) 14 Bromophos 18.68 331 > 316 (-15) 331 > 286 (-25) 15 Bromopropylate 26.13 183 > 155 (-15) 341 > 185 (-20) 16 Buprofezin 21.75 105 > 104 (-10) 105 > 77 (-15) 17 Butachlor 20.52 160 > 130 (-25) 176 > 147 (-15) 18 Cadusafos 13.08 159 > 97 (-20) 158 > 97 (-15) 19 Captan 19.64 79 > 77 (-10) 151 > 79 (-10) 20 Carbophenothion 23.90 199 > 143 (-10) 342 > 157 (-5) 21 Chinomethionat 20.25 234 > 206 (-10) 206 > 148 (-20) 22 Chlordane-cis 20.21 373 > 266 (-20) 375 > 266 (-20) 23 Chlordane-trans 20.66 373 > 266 (-20) 375 > 266 (-20) 24 Chlorfenapyr 22.10 59 > 31 (-5) 59 > 29 (-10) 25 Chlorfluazuron 20.83 321 > 304 (-30) 323 > 306 (-20) 26 Chlorobenzilate 22.66 251 > 139 (-15) 139 > 111 (-15) 27 Chlorothalonil 15.45 264 > 168 (-30) 266 > 231 (-20) 28 Chlorpropham 12.67 213 > 127 (-10) 127 > 65 (-20) 29 Chlorpyrifos 17.93 314 > 258 (-10) 316 > 260 (-10) 30 Chlorpyrifos-methyl 16.31 286 > 93 (-20) 288 > 93 (-25) 31 Chlorthal-dimethyl 18.10 301 > 223 (-20) 332 > 301 (-20)

234

No. Name tR (min)


32 Clomazone 14.22 125 > 289 (-15) 204 > 107 (-20) 33 Cyanophos 14.07 243 > 109 (-10) 109 > 79 (-10) 34 Cyflufenamid 22.12 91 > 65 (-15) 412 > 295 (-10)

35-1 Cyfluthrin_1 30.02 163 > 127 (-10) 163 > 91 (-20) 35-2 Cyfluthrin_2 30.14 163 > 127 (-10) 163 > 91 (-20) 35-3 Cyfluthrin_3 30.21 163 > 127 (-10) 163 > 91 (-20) 35-4 Cyfluthrin_4 30.27 163 > 127 (-10) 163 > 91 (-20) 36 Cyhalofop-butyl 27.72 357 > 256 (-10) 229 > 109 (-20) 37 Cyhalothrin-lambda 28.03 181 > 152 (-25) 197 > 141 (-15)

38-1 Cypermethrin_1 30.41 163 > 127 (-5) 181 > 152 (-20) 38-2 Cypermethrin_2 30.52 163 > 127 (-5) 181 > 152 (-20) 38-3 Cypermethrin_3 30.58 163 > 127 (-5) 181 > 152 (-20) 38-4 Cypermethrin_4 30.64 163 > 127 (-5) 181 > 152 (-20) 39 Cyproconazole 22.20 222 > 125 (-20) 139 > 111 (-20) 40 Cyprodinil 19.13 225 > 224 (-10) 224 > 208 (-20) 41 Daimuron 5.51 146 > 77 (-15) 146 > 105 (-10) 42 DDD-o,p' 21.71 235 > 165 (-20) 237 > 165 (-20) 43 DDD-p,p' 23.04 235 > 165 (-20) 237 > 165 (-20) 44 DDE-o,p' 20.32 246 > 176 (-35) 318 > 248 (-15) 45 DDE-p,p' 21.48 246 > 176 (-35) 318 > 248 (-15) 46 DDT-o,p' 22.72 235 > 165 (-20) 237 > 165 (-20) 47 DDT-p,p' 23.99 235 > 165 (-20) 237 > 165 (-20) 48 Di-allate 13.21 234 > 150 (-20) 234 > 192 (-15) 49 Diazinon 14.28 199 > 93 (-15) 179 > 122 (-25) 50 Dichlofluanid 17.60 224 > 123 (-10) 167 > 124 (-15) 51 Dichlorvos 7.59 109 > 79 (-5) 185 > 127 (-30) 52 Diclofop-methyl 24.89 340 > 253 (-15) 253 > 162 (-10) 53 Dicloran 13.79 206 > 176 (-10) 176 > 148 (-15) 54 Dicofol 18.46 139 > 111 (-15) 139 > 75 (-30) 55 Dieldrin 21.61 263 > 193 (-25) 279 > 207 (-25)

56-1 Difenoconazole_1 32.04 323 > 265 (-20) 323 > 202 (-25) 56-2 Difenoconazole_2 32.13 323 > 265 (-20) 323 > 202 (-25) 57 Dimepiperate 19.76 119 > 91 (-15) 145 > 112 (-10) 58 Dimethametryn 19.32 212 > 122 (-15) 212 > 94 (-25) 59 Dimethenamid 14.24 230 > 154 (-15) 154 > 111 (-15) 60 Diniconazole 22.82 268 > 232 (-15) 270 > 232 (-15)

235

No. Name tR (min)


61 Diphenamid 18.69 167 > 165 (-25) 239 > 167 (-10) 62 Diphenylamine 12.24 168 > 167 (-20) 169 > 168 (-20) 63 Disulfoton 15.09 88 > 60 (-10) 186 > 97 (-20) 64 Dithiopyr 17.01 354 > 286 (-15) 306 > 286 (-10) 65 Edifenphos 23.96 173 > 109 (-10) 310 > 173 (-15) 66 Endosulfan-alpha 20.66 241 > 206 (-20) 195 > 125 (-25) 67 Endosulfan-beta 22.73 241 > 206 (-15) 195 > 159 (-10) 68 Endosulfan-sulfate 24.09 272 > 237 (-15) 237 > 143 (-30) 69 Endrin 21.98 263 > 193 (-30) 281 > 245 (-10) 70 EPN 26.05 169 > 141 (-10) 157 > 110 (-15) 71 Esprocarb 17.65 222 > 91 (-15) 162 > 91 (-15) 72 Ethalfluralin 12.49 316 > 276 (-5) 333 > 276 (-15) 73 Ethion 22.96 231 > 129 (-20) 231 > 175 (-15) 74 Ethoprophos 11.63 158 > 97 (-20) 200 > 158 (-5) 75 Etoxazole 26.39 300 > 270 (-20) 359 > 187 (-25) 76 Etridiazole 9.85 211 > 183 (-10) 183 > 140 (-15) 77 Etrimfos 15.29 292 > 153 (-20) 277 > 125 (-15) 78 Fenamidone 26.45 268 > 180 (-25) 238 > 103 (-20) 79 Fenamiphos 20.91 303 > 195 (-10) 288 > 260 (-5) 80 Fenarimol 26.44 139 > 111 (-15) 251 > 139 (-15) 81 Fenazaquin 26.72 145 > 117 (-15) 160 > 145 (-10) 82 Fenbuconazole 29.95 129 > 102 (-15) 198 > 129 (-10) 83 Fenitrothion 17.37 277 > 260 (-10) 260 > 125 (-15) 84 Fenothiocarb 20.54 160 > 72 (-15) 160 > 106 (-10) 85 Fenpropathrin 26.44 181 > 152 (-25) 181 > 127 (-30) 86 Fenthion 18.06 278 > 109 (-15) 169 > 121 (-15)

87-1 Fenvalerate_1 31.45 167 > 125 (-10) 225 > 119 (-20) 87-2 Fenvalerate_2 31.72 167 > 125 (-10) 225 > 119 (-20) 88 Fipronil 19.34 367 > 213 (-25) 369 > 215 (-30)

89-1 Flucythrinate_1 30.59 199 > 157 (-10) 199 > 107 (-20) 89-2 Flucythrinate_2 30.82 199 > 157 (-10) 199 > 107 (-20) 90 Fludioxonil 21.22 248 > 127 (-25) 182 > 127 (-15) 91 Flutolanil 21.08 173 > 145 (-15) 281 > 173 (-15) 92 Folpet 19.89 260 > 130 (-15) 262 > 130 (-20)

93-1 Fosthiazate_1 18.69 195 > 103 (-10) 195 > 139 (-5) 93-2 Fosthiazate_2 18.78 195 > 103 (-10) 195 > 139 (-5)

236

No. Name tR (min)


94 Fthalide 18.52 243 > 215 (-20) 241 > 213 (-20) 95 Halfenprox 30.47 265 > 237 (-10) 265 > 117 (-10) 96 Heptachlor 16.80 272 > 237 (-15) 272 > 235 (-15) 97 Heptachlor-epoxide 19.37 353 > 263 (-15) 355 > 265 (-25) 98 Hexaconazole 21.12 214 > 172 (-20) 216 > 161 (-20) 99 Indanofan 26.54 139 > 111 (-20) 310 > 139 (-20)

100 Indoxacarb 33.46 218 > 203 (-10) 264 > 176 (-15) 101 Iprobenfos 15.58 204 > 91 (-10) 246 > 91 (-15) 102 Iprodione 25.38 314 > 245 (-15) 316 > 247 (-15) 103 Isazofos 15.13 161 > 119 (-25) 257 > 162 (-10) 104 Isofenphos 19.39 213 > 121 (-15) 255 > 121 (-25) 105 Isoprothiolane 21.21 231 > 189 (-10) 290 > 118 (-15) 106 Kresoxim-methyl 21.81 131 > 89 (-30) 206 > 131 (-10) 107 Lufenuron 9.39 353 > 203 (-15) 203 > 111 (-20) 108 Malathion 17.68 173 > 99 (-15) 158 > 125 (-10) 109 Mecarbam 19.53 131 > 74 (-20) 329 > 159 (-10) 110 Mefenacet 27.69 192 > 136 (-15) 192 > 109 (-25) 111 Mepronil 23.50 119 > 91 (-10) 269 > 119 (-20) 112 Metconazole 26.70 125 > 89 (-20) 125 > 99 (-15) 113 Methabenzthiazuron 12.18 164 > 136 (-15) 136 > 109 (-25) 114 Methidathion 20.12 145 > 85 (-5) 145 > 58 (-10) 115 Methoxychlor 26.32 227 > 169 (-25) 228 > 227 (-20) 116 Metobromuron 15.22 197 > 90 (-25) 199 > 171 (-15) 117 Metribuzin 15.84 198 > 82 (-25) 199 > 184 (-15) 118 Mevinphos 9.47 192 > 127 (-10) 127 > 95 (-15) 119 Molinate 11.05 126 > 55 (-10) 187 > 126 (-10) 120 Myclobutanil 21.63 179 > 125 (-15) 150 > 123 (-15) 121 Napropamide 20.95 128 > 72 (-10) 271 > 128 (-10) 122 Nitrapyrin 9.82 194 > 133 (-25) 194 > 158 (-20) 123 Nitrothal-isopropyl 18.51 236 > 194 (-15) 194 > 148 (-15) 124 Nuarimol 24.71 235 > 139 (-15) 314 > 139 (-15) 125 Ofurace 23.57 232 > 158 (-20) 281 > 158 (-5) 126 Oxadiazon 21.53 258 > 175 (-10) 258 > 112 (-25) 127 Oxadixyl 22.88 163 > 132 (-15) 163 > 117 (-25) 128 Oxyfluorfen 21.75 252 > 146 (-30) 361 > 300 (-15) 129 Paclobutrazol 20.43 236 > 125 (-10) 236 > 167 (-10)

237

No. Name tR (min)


130 Parathion 18.18 291 > 109 (-15) 261 > 109 (-10) 131 Parathion-methyl 16.51 263 > 109 (-15) 263 > 246 (-5) 132 Penconazole 19.29 248 > 157 (-25) 159 > 123 (-20) 133 Pendimethalin 19.09 252 > 162 (-15) 281 > 252 (-5) 134 Pentachloroaniline 15.88 263 > 193 (-25) 265 > 193 (-25) 135 Pentachlorothioanisole 17.54 296 > 263 (-10) 296 > 281 (-15) 136 Penthiopyrad 22.88 302 > 177 (-5) 177 > 101 (-20)

137-1 Permethrin_1 29.28 183 > 153 (-15) 183 > 168 (-15) 137-2 Permethrin_2 29.47 183 > 153 (-15) 183 > 168 (-15) 138 Phenthoate 19.61 274 > 125 (-20) 246 > 121 (-10) 139 Phorate 13.22 231 > 175 (-10) 260 > 75 (-10) 140 Phosalone 27.24 182 > 111 (-20) 367 > 182 (-10)

141-1 Phosphamidon_1 14.75 127 > 109 (-15) 264 > 127 (-20) 141-2 Phosphamidon_2 16.01 127 > 109 (-15) 264 > 127 (-20) 142 Piperophos 26.13 320 > 122 (-15) 140 > 98 (-10) 143 Pirimiphos-ethyl 18.70 318 > 166 (-15) 333 > 180 (-15) 144 Pirimiphos-methyl 17.30 290 > 125 (-20) 305 > 180 (-10)

145-1 Probenazole_1 14.35 159 > 130 (-15) 130 > 103 (-20) 145-2 Probenazole_2 16.57 159 > 130 (-15) 130 > 103 (-20) 146 Prochloraz 29.54 180 > 138 (-15) 180 > 69 (-15) 147 Procymidone 19.79 283 > 96 (-10) 285 > 96 (-15) 148 Profenofos 21.31 339 > 269 (-15) 337 > 267 (-15) 149 Prometryn 16.93 241 > 184 (-10) 226 > 184 (-10) 150 Propanil 16.23 161 > 99 (-25) 163 > 101 (-20)

151-1 Propiconazole_1 24.05 173 > 145 (-20) 259 > 173 (-10) 151-2 Propiconazole_2 24.27 173 > 145 (-20) 259 > 173 (-10) 152 Prothiofos 21.17 309 > 239 (-15) 267 > 239 (-10) 153 Pyraclofos 28.76 194 > 138 (-20) 360 > 194 (-15) 154 Pyrazophos 28.31 221 > 193 (-10) 232 > 204 (-10) 155 Pyridaben 29.48 147 > 117 (-20) 147 > 132 (-15) 156 Pyridalyl 30.82 204 > 148 (-20) 164 > 146 (-15) 157 Pyridaphenthion 25.70 340 > 199 (-15) 199 > 77 (-20) 158 Pyrimidifen 31.24 184 > 169 (-20) 161 > 135 (-15) 159 Pyriminobac-methyl E 23.93 302 > 256 (-20) 302 > 230 (-20) 160 Pyriminobac-methyl Z 22.64 302 > 256 (-20) 256 > 188 (-20) 161 Quintozene 14.29 295 > 237 (-15) 237 > 143 (-25)

238

No. Name tR (min)


162 Silafluofen 30.93 179 > 151 (-10) 179 > 169 (-10) 163 Simeconazole 16.55 211 > 121 (-15) 278 > 135 (-15) 164 Simetryn 16.69 213 > 170 (-10) 170 > 155 (-10) 165 Tebuconazole 24.79 250 > 125 (-25) 125 > 99 (-15) 166 Tebufenpyrad 26.63 333 > 171 (-25) 318 > 131 (-15) 167 Tebupirimfos 15.52 234 > 126 (-15) 276 > 234 (-10) 168 Tefluthrin 15.22 177 > 127 (-15) 197 > 141 (-10) 169 Terbufos 14.54 231 > 175 (-10) 288 > 231 (-5) 170 Terbuthylazine 14.24 214 > 104 (-20) 229 > 173 (-10) 171 Terbutryn 17.36 241 > 185 (-5) 226 > 96 (-25) 172 Tetraconazole 18.32 336 > 204 (-30) 338 > 206 (-30) 173 Tetradifon 27.03 159 > 131 (-10) 356 > 229 (-10) 174 Thiazopyr 17.76 327 > 277 (-25) 396 > 327 (-20) 175 Thifluzamide 21.61 194 > 166 (-10) 166 > 125 (-20) 176 Thiobencarb 17.96 100 > 72 (-5) 257 > 100 (-10) 177 Tolclofos-methyl 16.55 265 > 250 (-15) 250 > 220 (-15) 178 Tolylfluanid 19.39 238 > 137 (-15) 137 > 91 (-20) 179 Triadimefon 17.96 208 > 181 (-10) 181 > 99 (-20) 180 Triadimenol 19.80 168 > 70 (-10) 128 > 65 (-20) 181 Triazophos 23.50 161 > 134 (-10) 257 > 162 (-15) 182 Triflumizole 19.89 278 > 73 (-10) 287 > 218 (-15) 183 Triflumuron 8.32 139 > 111 (-15) 155 > 139 (-10) 184 Trifluralin 12.73 306 > 264 (-10) 264 > 160 (-20) 185 Vinclozolin 16.45 285 > 212 (-15) 287 > 214 (-10)

186-1 Zoxamide_1 25.05 187 > 159 (-15) 189 > 161 (-15) 186-2 Zoxamide_2 25.36 187 > 159 (-15) 189 > 161 (-15)

239

Table S3. List of general pesticide information for 384 pesticides

No. Compound name Activity group Chemical group Remarks 1 2-(1-naphthyl)acetamide Plant growth regulator Others/Unclassified 2 2,6-Diisopropylnaphthalene Plant growth regulator Others/Unclassified 3 2-phenylphenol Fungicide Others/Unclassified Korea MRLs in food (2017) 4 Acetochlor Herbicide Others/Unclassified Korea MRLs in food (2017) 5 Acibenzolar-S-methyl Others Others/Unclassified Korea MRLs in food (2017) 6 Acrinathrin Insecticide Pyrethroid Korea MRLs in food (2017) 7 Alachlor Herbicide Others/Unclassified Korea MRLs in food (2017) 8 Alachlor-2-hydroxy Herbicide Others/Unclassified Alachlor metabolite 9 Aldrin Insecticide Organochlorine Korea MRLs in food (2017)

10 Allethrin Insecticide Pyrethroid 11 Allidochlor Herbicide Others/Unclassified 12 Ametryn Herbicide Triazine 13 Anilofos Herbicide Organophosphate Korea MRLs in food (2017) 14 Aramite Insecticide Others/Unclassified 15 Aspon Insecticide Organophosphate 16 Atrazine Herbicide Triazine 17 Azaconazole Fungicide Triazole 18 Azinphos-ethyl Insecticide Organophosphate 19 Azinphos-methyl Insecticide Organophosphate Korea MRLs in food (2017) 20 Benalaxyl Fungicide Others/Unclassified Korea MRLs in food (2017) 21 Benfluralin Herbicide Others/Unclassified 22 Benodanil Fungicide Others/Unclassified 23 Benoxacor Others Others/Unclassified

240

No. Compound name Activity group Chemical group Remarks 24 Benzoylprop-ethyl Herbicide Others/Unclassified 25 BHC-alpha Insecticide Organochlorine Korea MRLs in food (2017) 26 BHC-beta Insecticide Organochlorine Korea MRLs in food (2017) 27 BHC-delta Insecticide Organochlorine Korea MRLs in food (2017) 28 BHC-gamma Insecticide Organochlorine Korea MRLs in food (2017) 29 Bifenox Herbicide Others/Unclassified Korea MRLs in food (2017) 30 Bifenthrin Insecticide Pyrethroid Korea MRLs in food (2017) 31 Binapacryl Inter-activity Others/Unclassified 32 Biphenyl Fungicide Others/Unclassified 33 Bromacil Herbicide Others/Unclassified Korea MRLs in food (2017) 34 Bromobutide Herbicide Others/Unclassified Korea MRLs in food (2017) 35 Bromophos Insecticide Organophosphate 36 Bromophos-ethyl Insecticide Organophosphate 37 Bromopropylate Insecticide Organochlorine Korea MRLs in food (2017) 38 BTS 27919 Insecticide Others/Unclassified Amitraz metabolite 39 Bupirimate Fungicide Others/Unclassified 40 Buprofezin Insecticide Others/Unclassified Korea MRLs in food (2017) 41 Butachlor Herbicide Others/Unclassified Korea MRLs in food (2017) 42 Butafenacil Herbicide Others/Unclassified 43 Butralin Inter-activity Others/Unclassified 44 Butylate Herbicide Carbamate 45 Cadusafos Insecticide Organophosphate Korea MRLs in food (2017) 46 Carbophenothion Insecticide Organophosphate Korea MRLs in food (2017) 47 Carbosulfan Insecticide Carbamate Korea MRLs in food (2017) 48 Chinomethionat Inter-activity Others/Unclassified Korea MRLs in food (2017)

241

No. Compound name Activity group Chemical group Remarks 49 Chlorbenside Insecticide Organochlorine 50 Chlorbufam Herbicide Carbamate 51 Chlordane-cis Insecticide Organochlorine Korea MRLs in food (2017) 52 Chlordane-trans Insecticide Organochlorine Korea MRLs in food (2017) 53 Chlordimeform Insecticide Others/Unclassified 54 Chlorethoxyfos Insecticide Organophosphate 55 Chlorfenapyr Insecticide Organochlorine Korea MRLs in food (2017) 56 Chlorfenson Insecticide Organochlorine 57 Chlorfluazuron Insecticide Urea Korea MRLs in food (2017) 58 Chlorflurenol-methyl Inter-activity Others/Unclassified 59 Chloridazon Herbicide Organochlorine 60 Chlormephos Insecticide Organophosphate 61 Chlornitrofen Herbicide Organochlorine 62 Chlorobenzilate Insecticide Organochlorine Korea MRLs in food (2017) 63 Chloroneb Fungicide Organochlorine 64 Chloropropylate Insecticide Organochlorine 65 Chlorothalonil Fungicide Organochlorine Korea MRLs in food (2017) 66 Chloroxuron Herbicide Urea 67 Chlorpropham Inter-activity Carbamate Korea MRLs in food (2017) 68 Chlorpyrifos Insecticide Organophosphate Korea MRLs in food (2017) 69 Chlorpyrifos-methyl Insecticide Organophosphate Korea MRLs in food (2017) 70 Chlorthal-dimethyl Herbicide Organochlorine 71 Chlorthion Insecticide Organophosphate 72 Chlorthiophos Insecticide Organophosphate 73 Chlozolinate Fungicide Organochlorine

242

No. Compound name Activity group Chemical group Remarks 74 Cinidon-ethyl Herbicide Others/Unclassified 75 Cinmethylin Herbicide Others/Unclassified 76 Clomazone Herbicide Others/Unclassified Korea MRLs in food (2017) 77 Clomeprop Herbicide Others/Unclassified 78 Coumaphos Insecticide Organophosphate 79 Crotoxyphos Insecticide Organophosphate 80 Crufomate Insecticide Organophosphate 81 Cyanazine Herbicide Triazine 82 Cyanofenphos Insecticide Organophosphate 83 Cyanophos Insecticide Organophosphate 84 Cycloate Herbicide Carbamate 85 Cycloxydim Herbicide Others/Unclassified 86 Cyenopyrafen Insecticide Others/Unclassified Korea MRLs in food (2017) 87 Cyflufenamid Fungicide Others/Unclassified Korea MRLs in food (2017) 88 Cyflumetofen Insecticide Others/Unclassified Korea MRLs in food (2017) 89 Cyfluthrin Insecticide Pyrethroid Korea MRLs in food (2017) 90 Cyhalofop-butyl Herbicide Others/Unclassified Korea MRLs in food (2017) 91 Cyhalothrin-lambda Insecticide Pyrethroid Korea MRLs in food (2017) 92 Cypermethrin Insecticide Pyrethroid Korea MRLs in food (2017) 93 Cyprazine Herbicide Triazine 94 Cyproconazole Fungicide Triazole Korea MRLs in food (2017) 95 Cyprodinil Fungicide Others/Unclassified Korea MRLs in food (2017) 96 DDD-o,p' Insecticide Organochlorine DDT metabolite 97 DDD-p,p' Insecticide Organochlorine DDT metabolite 98 DDE-o,p' Insecticide Organochlorine DDT metabolite

243

No. Compound name Activity group Chemical group Remarks 99 DDE-p,p' Insecticide Organochlorine DDT metabolite

100 DDT-o,p' Insecticide Organochlorine Korea MRLs in food (2017) 101 DDT-p,p' Insecticide Organochlorine Korea MRLs in food (2017) 102 Deltamethrin Insecticide Pyrethroid Korea MRLs in food (2017) 103 Demeton-O Insecticide Organophosphate 104 Demeton-S Insecticide Organophosphate 105 Demeton-S-methyl-sulfone Insecticide Organophosphate 106 Desmedipham Herbicide Carbamate 107 Desmetryn Herbicide Triazine 108 Dialifor Insecticide Organophosphate 109 Di-allate Herbicide Carbamate 110 Diazinon Insecticide Organophosphate Korea MRLs in food (2017) 111 Dichlobenil Herbicide Organochlorine Korea MRLs in food (2017) 112 Dichlofenthion Insecticide Organophosphate 113 Dichlofluanid Fungicide Organochlorine Korea MRLs in food (2017) 114 Dichlormid Others Others/Unclassified 115 Dichlorvos Insecticide Organophosphate Korea MRLs in food (2017) 116 Diclobutrazol Fungicide Triazole 117 Diclofop-methyl Herbicide Organochlorine Korea MRLs in food (2017) 118 Dicloran Fungicide Organochlorine Korea MRLs in food (2017) 119 Dicofol Insecticide Organochlorine Korea MRLs in food (2017) 120 Dieldrin Insecticide Organochlorine Korea MRLs in food (2017) 121 Diethatyl-ethyl Herbicide Others/Unclassified 122 Diethofencarb Fungicide Carbamate Korea MRLs in food (2017) 123 Difenoconazole Fungicide Triazole Korea MRLs in food (2017)

244

No. Compound name Activity group Chemical group Remarks 124 Diflufenican Herbicide Others/Unclassified 125 Dimepiperate Herbicide Carbamate Korea MRLs in food (2017) 126 Dimethachlor Herbicide Others/Unclassified 127 Dimethametryn Herbicide Triazine Korea MRLs in food (2017) 128 Dimethenamid Herbicide Others/Unclassified Korea MRLs in food (2017) 129 Dimethoate Insecticide Organophosphate Korea MRLs in food (2017) 130 Dimethylvinphos Insecticide Organophosphate Korea MRLs in food (2017) 131 Diniconazole Fungicide Triazole Korea MRLs in food (2017) 132 Dinitramine Herbicide Others/Unclassified 133 Dinobuton Inter-activity Others/Unclassified 134 Dioxabenzofos Insecticide Organophosphate 135 Dioxacarb Insecticide Carbamate 136 Dioxathion Insecticide Organophosphate 137 Diphenamid Herbicide Others/Unclassified Korea MRLs in food (2017) 138 Diphenylamine Fungicide Others/Unclassified Korea MRLs in food (2017) 139 Disulfoton Insecticide Organophosphate Korea MRLs in food (2017) 140 Dithiopyr Herbicide Others/Unclassified Korea MRLs in food (2017) 141 Edifenphos Fungicide Organophosphate Korea MRLs in food (2017) 142 Endosulfan-alpha Insecticide Organochlorine Korea MRLs in food (2017) 143 Endosulfan-beta Insecticide Organochlorine Korea MRLs in food (2017) 144 Endosulfan-sulfate Insecticide Organochlorine Endosulfan metabolite 145 Endrin Insecticide Organochlorine Korea MRLs in food (2017) 146 EPN Insecticide Organophosphate Korea MRLs in food (2017) 147 Epoxiconazole Fungicide Triazole Korea MRLs in food (2017) 148 EPTC Herbicide Carbamate

245

No. Compound name Activity group Chemical group Remarks 149 Esprocarb Herbicide Carbamate Korea MRLs in food (2017) 150 Etaconazole Fungicide Triazole 151 Ethalfluralin Herbicide Others/Unclassified Korea MRLs in food (2017) 152 Ethion Insecticide Organophosphate Korea MRLs in food (2017) 153 Ethofumesate Herbicide Others/Unclassified 154 Ethoprophos Insecticide Organophosphate Korea MRLs in food (2017) 155 Ethychlozate Plant growth regulator Others/Unclassified Korea MRLs in food (2017) 156 Etofenprox Insecticide Pyrethroid Korea MRLs in food (2017) 157 Etoxazole Insecticide Others/Unclassified Korea MRLs in food (2017) 158 Etridiazole Fungicide Organochlorine Korea MRLs in food (2017) 159 Etrimfos Insecticide Organophosphate Korea MRLs in food (2017) 160 Famphur Insecticide Organophosphate 161 Fenamidone Fungicide Others/Unclassified Korea MRLs in food (2017) 162 Fenamiphos Insecticide Organophosphate Korea MRLs in food (2017) 163 Fenarimol Fungicide Organochlorine Korea MRLs in food (2017) 164 Fenazaquin Insecticide Others/Unclassified Korea MRLs in food (2017) 165 Fenbuconazole Fungicide Triazole Korea MRLs in food (2017) 166 Fenchlorphos Insecticide Organophosphate 167 Fenfuram Fungicide Others/Unclassified 168 Fenitrothion Insecticide Organophosphate Korea MRLs in food (2017) 169 Fenobucarb Insecticide Carbamate Korea MRLs in food (2017) 170 Fenothiocarb Insecticide Carbamate Korea MRLs in food (2017) 171 Fenoxanil Fungicide Others/Unclassified Korea MRLs in food (2017) 172 Fenoxycarb Insecticide Carbamate Korea MRLs in food (2017) 173 Fenpropathrin Insecticide Pyrethroid Korea MRLs in food (2017)

246

No. Compound name Activity group Chemical group Remarks 174 Fenpropidin Fungicide Others/Unclassified 175 Fenpropimorph Fungicide Others/Unclassified 176 Fenpyrazamine Fungicide Others/Unclassified Korea MRLs in food (2017) 177 Fenson Insecticide Organochlorine 178 Fensulfothion Insecticide Organophosphate Korea MRLs in food (2017) 179 Fenthion Insecticide Organophosphate Korea MRLs in food (2017) 180 Fenvalerate Insecticide Pyrethroid Korea MRLs in food (2017) 181 Fipronil Insecticide Others/Unclassified Korea MRLs in food (2017) 182 Flamprop-isopropyl Herbicide Others/Unclassified 183 Flamprop-methyl Herbicide Others/Unclassified 184 Flonicamid Insecticide Others/Unclassified Korea MRLs in food (2017) 185 Fluazifop-butyl Herbicide Others/Unclassified Korea MRLs in food (2017) 186 Fluchloralin Herbicide Others/Unclassified 187 Flucythrinate Insecticide Pyrethroid Korea MRLs in food (2017) 188 Fludioxonil Fungicide Others/Unclassified Korea MRLs in food (2017) 189 Flufenpyr-ethyl Herbicide Others/Unclassified 190 Flumetralin Plant growth regulator Others/Unclassified 191 Flumiclorac-pentyl Herbicide Others/Unclassified 192 Flumioxazin Herbicide Others/Unclassified Korea MRLs in food (2017) 193 Fluopyram Fungicide Others/Unclassified Korea MRLs in food (2017) 194 Fluorodifen Herbicide Others/Unclassified 195 Flupyradifurone Insecticide Others/Unclassified Korea MRLs in food (2017) 196 Flurochloridone Herbicide Others/Unclassified 197 Flurtamone Herbicide Others/Unclassified 198 Flusilazole Fungicide Triazole Korea MRLs in food (2017)

247

No. Compound name Activity group Chemical group Remarks 199 Fluthiacet-methyl Herbicide Others/Unclassified 200 Flutianil Fungicide Others/Unclassified Korea MRLs in food (2017) 201 Flutolanil Fungicide Others/Unclassified Korea MRLs in food (2017) 202 Flutriafol Fungicide Triazole 203 Fluvalinate Insecticide Pyrethroid Korea MRLs in food (2017) 204 Folpet Fungicide Organochlorine Korea MRLs in food (2017) 205 Fonofos Insecticide Organophosphate 206 Formothion Insecticide Organophosphate Korea MRLs in food (2017) 207 Fosthiazate Insecticide Organophosphate Korea MRLs in food (2017) 208 Fthalide Fungicide Organochlorine Korea MRLs in food (2017) 209 Furathiocarb Insecticide Carbamate Korea MRLs in food (2017) 210 Furilazole Others Others/Unclassified 211 Halfenprox Insecticide Pyrethroid Korea MRLs in food (2017) 212 Heptachlor Insecticide Organochlorine Korea MRLs in food (2017) 213 Heptachlor-epoxide Insecticide Organochlorine Heptachlor metabolite 214 Heptenophos Insecticide Organophosphate 215 Hexachlorobenzene Fungicide Organochlorine 216 Hexaconazole Fungicide Triazole Korea MRLs in food (2017) 217 Imazalil Fungicide Organochlorine Korea MRLs in food (2017) 218 Imazamethabenz-methyl Herbicide Others/Unclassified 219 Indanofan Herbicide Others/Unclassified Korea MRLs in food (2017) 220 Indoxacarb Insecticide Others/Unclassified Korea MRLs in food (2017) 221 Iprobenfos Fungicide Organophosphate Korea MRLs in food (2017) 222 Iprodione Inter-activity Organochlorine Korea MRLs in food (2017) 223 Iprovalicarb Fungicide Carbamate Korea MRLs in food (2017)

248

No. Compound name Activity group Chemical group Remarks 224 Isazofos Insecticide Organophosphate Korea MRLs in food (2017) 225 Isofenphos Insecticide Organophosphate Korea MRLs in food (2017) 226 Isofenphos-methyl Insecticide Organophosphate 227 Isopropalin Herbicide Others/Unclassified 228 Isoprothiolane Inter-activity Others/Unclassified Korea MRLs in food (2017) 229 Isotianil Inter-activity Others/Unclassified Korea MRLs in food (2017) 230 Isoxadifen-ethyl Others Others/Unclassified 231 Isoxathion Insecticide Organophosphate 232 Kresoxim-methyl Fungicide Others/Unclassified Korea MRLs in food (2017) 233 Lactofen Herbicide Others/Unclassified 234 Leptophos Insecticide Organophosphate 235 Malathion Insecticide Organophosphate Korea MRLs in food (2017) 236 Mecarbam Insecticide Organophosphate Korea MRLs in food (2017) 237 Mefenacet Herbicide Others/Unclassified Korea MRLs in food (2017) 238 Mefenpyr-diethyl Others Others/Unclassified 239 Mephosfolan Insecticide Organophosphate 240 Mepronil Fungicide Others/Unclassified Korea MRLs in food (2017) 241 Metazachlor Herbicide Organochlorine 242 Metconazole Fungicide Triazole Korea MRLs in food (2017) 243 Methidathion Insecticide Organophosphate Korea MRLs in food (2017) 244 Methoprotryne Herbicide Triazine 245 Methoxychlor Insecticide Organochlorine Korea MRLs in food (2017) 246 Methyl 3,5-dichlorobenzoate Inter-activity Others/Unclassified 247 Methyl trithion Insecticide Organophosphate 248 Metolachlor Herbicide Others/Unclassified Korea MRLs in food (2017)

249

No. Compound name Activity group Chemical group Remarks 249 Metoxuron Herbicide Urea 250 Metrafenone Fungicide Others/Unclassified Korea MRLs in food (2017) 251 Metribuzin Herbicide Triazine Korea MRLs in food (2017) 252 Mevinphos Insecticide Organophosphate Korea MRLs in food (2017) 253 MGK-264 Others Others/Unclassified 254 Mirex Insecticide Organochlorine 255 Molinate Herbicide Carbamate Korea MRLs in food (2017) 256 Monolinuron Herbicide Urea 257 Myclobutanil Fungicide Triazole Korea MRLs in food (2017) 258 Napropamide Herbicide Others/Unclassified Korea MRLs in food (2017) 259 Neburon Herbicide Urea 260 N-Ethyl-p-toluene sulfonamide Herbicide Others/Unclassified 261 Nitralin Herbicide Others/Unclassified 262 Nitrapyrin Fungicide Organochlorine Korea MRLs in food (2017) 263 Nitrothal-isopropyl Fungicide Others/Unclassified 264 Nonachlor-cis Insecticide Organochlorine 265 Nonachlor-trans Insecticide Organochlorine 266 Norflurazon Herbicide Others/Unclassified Korea MRLs in food (2017) 267 Noruron Herbicide Urea 268 Nuarimol Fungicide Organochlorine Korea MRLs in food (2017) 269 Octhilinone Fungicide Others/Unclassified 270 Ofurace Fungicide Others/Unclassified Korea MRLs in food (2017) 271 Oryzalin Herbicide Others/Unclassified Korea MRLs in food (2017) 272 Oxadiazon Herbicide Others/Unclassified Korea MRLs in food (2017) 273 Oxadixyl Fungicide Others/Unclassified Korea MRLs in food (2017)

250

No. Compound name Activity group Chemical group Remarks 274 Oxycarboxin Fungicide Others/Unclassified 275 Oxychlordane Insecticide Organochlorine Chlordane metabolite 276 Oxyfluorfen Herbicide Others/Unclassified Korea MRLs in food (2017) 277 Paclobutrazol Plant growth regulator Triazole Korea MRLs in food (2017) 278 Parathion Insecticide Organophosphate Korea MRLs in food (2017) 279 Parathion-methyl Insecticide Organophosphate Korea MRLs in food (2017) 280 Pebulate Herbicide Carbamate 281 Penconazole Fungicide Triazole Korea MRLs in food (2017) 282 Pendimethalin Herbicide Others/Unclassified Korea MRLs in food (2017) 283 Penflufen Fungicide Others/Unclassified Korea MRLs in food (2017) 284 Pentachloroaniline Fungicide Organochlorine Quintozene metabolite 285 Pentachlorobenzene Fungicide Organochlorine Quintozene metabolite 286 Pentachlorobenzonitrile Fungicide Organochlorine Chlorothalonil metabolite 287 Pentachlorothioanisole Fungicide Organochlorine Quintozene metabolite 288 Penthiopyrad Fungicide Others/Unclassified Korea MRLs in food (2017) 289 Pentoxazone Herbicide Others/Unclassified Korea MRLs in food (2017) 290 Permethrin Insecticide Pyrethroid Korea MRLs in food (2017) 291 Perthane Insecticide Organochlorine 292 Phenothrin Insecticide Pyrethroid Korea MRLs in food (2017) 293 Phenthoate Insecticide Organophosphate Korea MRLs in food (2017) 294 Phorate Insecticide Organophosphate Korea MRLs in food (2017) 295 Phosalone Insecticide Organophosphate Korea MRLs in food (2017) 296 Phosfolan Insecticide Organophosphate 297 Phosmet Insecticide Organophosphate Korea MRLs in food (2017) 298 Phosphamidon Insecticide Organophosphate Korea MRLs in food (2017)

251

No. Compound name Activity group Chemical group Remarks 299 Picolinafen Herbicide Others/Unclassified 300 Picoxystrobin Fungicide Others/Unclassified Korea MRLs in food (2017) 301 Piperophos Herbicide Organophosphate Korea MRLs in food (2017) 302 Pirimicarb Insecticide Carbamate Korea MRLs in food (2017) 303 Pirimiphos-ethyl Insecticide Organophosphate Korea MRLs in food (2017) 304 Pirimiphos-methyl Insecticide Organophosphate Korea MRLs in food (2017) 305 Pretilachlor Herbicide Others/Unclassified Korea MRLs in food (2017) 306 Probenazole Others Others/Unclassified Korea MRLs in food (2017) 307 Prochloraz Fungicide Organochlorine Korea MRLs in food (2017) 308 Procymidone Fungicide Organochlorine Korea MRLs in food (2017) 309 Profenofos Insecticide Organophosphate Korea MRLs in food (2017) 310 Profluralin Herbicide Others/Unclassified 311 Prometon Herbicide Triazine 312 Prometryn Herbicide Triazine Korea MRLs in food (2017) 313 Propachlor Herbicide Others/Unclassified 314 Propanil Herbicide Others/Unclassified Korea MRLs in food (2017) 315 Propargite Insecticide Others/Unclassified Korea MRLs in food (2017) 316 Propazine Herbicide Triazine 317 Propetamphos Insecticide Organophosphate 318 Propham Inter-activity Carbamate 319 Propiconazole Fungicide Triazole Korea MRLs in food (2017) 320 Propisochlor Herbicide Others/Unclassified Korea MRLs in food (2017) 321 Propyzamide Herbicide Others/Unclassified 322 Proquinazid Fungicide Others/Unclassified 323 Prosulfocarb Herbicide Carbamate

252

No. Compound name Activity group Chemical group Remarks 324 Prothiofos Insecticide Organophosphate Korea MRLs in food (2017) 325 Pyracarbolid Fungicide Others/Unclassified 326 Pyraclofos Insecticide Organophosphate Korea MRLs in food (2017) 327 Pyrazophos Fungicide Organophosphate Korea MRLs in food (2017) 328 Pyridaben Insecticide Others/Unclassified Korea MRLs in food (2017) 329 Pyridalyl Insecticide Others/Unclassified Korea MRLs in food (2017) 330 Pyridaphenthion Insecticide Organophosphate Korea MRLs in food (2017) 331 Pyrifenox Fungicide Others/Unclassified 332 Pyrifluquinazon Insecticide Others/Unclassified Korea MRLs in food (2017) 333 Pyrimidifen Insecticide Others/Unclassified Korea MRLs in food (2017) 334 Pyriminobac-methyl E Herbicide Others/Unclassified Korea MRLs in food (2017) 335 Pyriminobac-methyl Z Herbicide Others/Unclassified Korea MRLs in food (2017) 336 Quinalphos Insecticide Organophosphate Korea MRLs in food (2017) 337 Quinoxyfen Fungicide Others/Unclassified 338 Quintozene Fungicide Organochlorine Korea MRLs in food (2017) 339 Secbumeton Herbicide Triazine 340 Silafluofen Insecticide Pyrethroid Korea MRLs in food (2017) 341 Simeconazole Fungicide Triazole Korea MRLs in food (2017) 342 Simetryn Herbicide Triazine Korea MRLs in food (2017) 343 Spiroxamine Fungicide Others/Unclassified Korea MRLs in food (2017) 344 Sulfallate Herbicide Carbamate 345 Sulfotep Insecticide Organophosphate 346 Sulprofos Insecticide Organophosphate 347 TCMTB Fungicide Others/Unclassified 348 Tebuconazole Fungicide Triazole Korea MRLs in food (2017)

253

No. Compound name Activity group Chemical group Remarks 349 Tebufenpyrad Insecticide Others/Unclassified Korea MRLs in food (2017) 350 Tebupirimfos Insecticide Organophosphate Korea MRLs in food (2017) 351 Tecnazene Inter-activity Organochlorine Korea MRLs in food (2017) 352 Tefluthrin Insecticide Pyrethroid Korea MRLs in food (2017) 353 Terbacil Herbicide Others/Unclassified 354 Terbufos Insecticide Organophosphate Korea MRLs in food (2017) 355 Terbumeton Herbicide Triazine 356 Terbuthylazine Herbicide Triazine Korea MRLs in food (2017) 357 Terbutryn Herbicide Triazine Korea MRLs in food (2017) 358 Tetrachlorvinphos Insecticide Organophosphate 359 Tetraconazole Fungicide Triazole Korea MRLs in food (2017) 360 Tetradifon Insecticide Organochlorine Korea MRLs in food (2017) 361 Tetramethrin Insecticide Pyrethroid 362 Tetrasul Insecticide Organochlorine 363 Thiazopyr Herbicide Others/Unclassified Korea MRLs in food (2017) 364 Thifluzamide Fungicide Others/Unclassified Korea MRLs in food (2017) 365 Thiometon Insecticide Organophosphate Korea MRLs in food (2017) 366 Thionazin Insecticide Organophosphate 367 Tolclofos-methyl Fungicide Organophosphate Korea MRLs in food (2017) 368 Tolfenpyrad Insecticide Others/Unclassified Korea MRLs in food (2017) 369 Tolylfluanid Fungicide Organochlorine Korea MRLs in food (2017) 370 Tralomethrin Insecticide Pyrethroid Korea MRLs in food (2017) 371 Triadimefon Fungicide Triazole Korea MRLs in food (2017) 372 Triadimenol Fungicide Triazole Korea MRLs in food (2017) 373 Tri-allate Herbicide Carbamate Korea MRLs in food (2017)

254

No. Compound name Activity group Chemical group Remarks 374 Triazophos Insecticide Organophosphate Korea MRLs in food (2017) 375 Tribufos Inter-activity Organophosphate 376 Trichloronat Insecticide Organophosphate 377 Tridiphane Herbicide Others/Unclassified 378 Triflumizole Fungicide Others/Unclassified Korea MRLs in food (2017) 379 Triflumuron Insecticide Urea Korea MRLs in food (2017) 380 Trifluralin Herbicide Others/Unclassified Korea MRLs in food (2017) 381 Uniconazole Plant growth regulator Triazole 382 Vernolate Herbicide Carbamate 383 Vinclozolin Fungicide Organochlorine Korea MRLs in food (2017) 384 Zoxamide Fungicide Others/Unclassified Korea MRLs in food (2017)

255

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초 록

서울대학교 대학원

농생명공학부 응용생명화학전공

신용호

농약은 지난 수십 년 동안 작물 중 해충, 선충, 미생물 및 잡초

등의 방제에 활용되었으며 인류의 식량 안보에 지대한 공헌을

하였다. 그러나 농약은 본질적으로 인체독성 및 생태독성을 가지고

있다. 따라서 다종(多種)∙다수(多數)의 농약에 대해 인체 내, 환경 및

농산물 중 농약 잔류 수준을 지속적으로 모니터링하여 그 양상을

신속하고 정확하게 파악할 필요가 있다. 본 연구에서는 기체 및

액체크로마토그래피-탠덤질량분석법을 활용하여 생체 시료(혈청 및

소변), 양봉 시료(꿀벌, 화분 및 꿀) 및 대표작물(고추, 오렌지, 현미

및 대두) 중 약 사백여 개의 다종농약다성분을 동시분석하였다.

개별 농약성분에 대한 최적의 감도 및 선택성을 확보하고자

탠덤질량분석기의 multiple reaction monitoring (MRM) 방법을

활용하였다. 생체 시료인 혈청 및 소변에 대한 전처리는 세 가지

형태의 “Quick, Easy, Cheap, Effective, Rugged, and Safe”

(QuEChERS)법을 시료량 및 용매량을 축소하여 비교하였다. 이중

최적의 전처리법을 선택한 후, LC-MS/MS를 활용하여 혈청 중

379성분 및 소변 중 380성분에 대한 분석법을 검증하였으며, 그

결과 전체 농약 성분 중 94.5% (혈청) 및 95.8% (소변)가 정량한계

10 ng/mL를 만족하였다. 또한 확립된 전처리법을 GC-MS/MS 대상

농약 성분(혈청; 54개, 소변; 55개)에 적용하여 이중 각각 53성분에

대해 10 ng/mL를 만족하여, 농약 중독 또는 농작업자 농약 살포시

생체시료를 통한 원인 규명 및 체내 노출량을 파악할 수 있는

충분한 감도를 확보하였다. 양봉시료인 꿀벌(사충, 생충 및 유충),

화분 및 꿀은 QuEChERS법을 최적화하여 전처리하였다.

274

유럽연합(EU)에서 2018년 말부터 옥외 사용이 전면 금지될 것으로

예상되는 네오니코티노이드(neonicotinoid) 계열 농약 클로티아니딘

(clothianidin), 이미다클로프리드(imidacloprid) 및 티아메톡삼

(thiamethoxam)에 대해 분석법을 검증하였으며, 3종 시료에서 모두

정량한계 1 ng/g을 만족하였다. 이는 벌의 급성경구독성 (LD50)보다

충분히 낮은 수준으로 농약에 의한 생태독성을 충분히 모니터링할

수 있었다. 2014년 두 지역(사과과수원 및 고추밭 일대)에서

모니터링을 수행하여 수집한 양봉 시료에 대해 네오니코티노이드

분석 및 다종농약 391성분 스크리닝 분석을 실시하였다. 분석

결과를 바탕으로 꿀벌이 잔류 농약에 노출되는 양상을 확인하고

검출된 농약 중 일부 성분에 대해 위해성 평가(risk assessment)를

실시하였다. 대표작물 4종은 식품공전 다종농약다성분 분석법-

제2법의 시료량 및 용매량을 축소하여 전처리하였으며, GC-MS/MS를

사용하여 농약 384성분에 대해 분석법을 평가하였다. 그 결과 전체

농약 성분 중 95.1-99.5%가 분석법상 정량한계(method limit of

quantitation) 10 ng/g 이하를 만족하여 농약 허용물질목록

관리제도(positive list system)가 요구하는 잔류농약 분석 감도(≤10

ng/g)를 확보하였다.

주요어 : 기체크로마토그래피-탠덤질량분석, 꿀, 꿀벌, 농산물,

농약, 다종농약다성분, 소변, 액체크로마토그래피-탠덤질량분석,

화분, 혈청

학 번 : 2014-21899

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